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The unique synaptic circuitry of specialized olfactory glomeruli in Drosophila melanogaster

Lydia Gruber, Rafael Cantera, Markus William Pleijzier, Michael Steinert, Thomas Pertsch, Bill S. Hansson, Jürgen Rybak

Preprint posted on 25 April 2023 https://www.biorxiv.org/content/10.1101/2022.09.30.510181v3

Ever tried untangling a plate of pasta? This preprint tries to make sense of the spaghetti-like connections of narrowly and broadly tuned olfactory glomeruli in the Drosophila brain.

Selected by T. W. Schwanitz

Introduction to untangling pasta (neural connectomics)

Neuroscience borrows a lot of terminology from electrical engineering, like “circuit motifs” and “wiring diagrams.” Words like these suggest straight lines—with inputs connecting to outputs in an orderly manner. But in reality, terms like “pasta plate” or “spaghetti diagram” would be more apt for describing how neurons are connected in the brain of living organisms. The brain is wetware, not hardware. A recent study by Gruber and colleagues uses laser branding and electron microscopy to try to make sense of this welter of living wires. The authors investigated the structure of the olfactory regions in the Drosophila melanogaster brain that respond to specific odors, as compared with those that respond to many odors.

In insects (and similarly in vertebrates), olfactory sensory neurons on the antennae converge in a region called the antennal lobe. Within this region, neurons detecting a particular odorant or group of odorants form spherical clusters known as glomeruli, which literally means “little balls of thread.” Projection neurons transmit information from glomeruli to higher brain centers. Local interneurons connect glomeruli together, allowing them to modulate each other’s activity.

That was the simple wiring diagram description. Now for the details that make this more like a spaghetti plate: neurons can synapse onto themselves; most neuronal outputs connect to multiple inputs (termed polyadic synapses, see Fig. 1 of the preprint); dendrites responsible for input processing also have outputs (dendro-dendritic synapses); and, finally, a whole smorgasbord of neurotransmitters and neuromodulators add additional subtlety and complexity—akin to the seasoning on pasta.

Gruber and colleagues wondered if glomeruli that respond to only a few odors have common features that differ from glomeruli that respond to many odors. They used focused ion beam-scanning electron microscopy to investigate the DA2 glomerulus, which is narrowly tuned and responds to the highly aversive odorant geosmin, and the DL5 glomerulus, which is broadly tuned to a number of aversive odorants such as benzaldehyde (I keep them straight by remembering that the one with the bigger number responds to more odorants). The researchers added VA1v to their comparison, a narrowly tuned glomerulus that responds to the aggregation signal methyl laurate. The scientists took images at 20 nanometer distance intervals (unimaginably small steps) via focused ion beam-scanning electron microscopy, and they then reconstructed 3D volumes via computer software. To keep track of the location of their glomeruli of interest, the authors used a two-photon laser to brand lines in the brain around the glomeruli—thereby making it easier to find the edges of the glomeruli in all those virtual slices.

 

Fig. 1. Overview of the glomerular structure. A) and B) both show the two main glomeruli investigated in this study via in vivo functional imaging. The thin green line above and below both glomeruli is the laser branding mark. C) and D) show these glomeruli as scanning electron microscope images. In D), the white triangle in the bottom right points to a thin black line that corresponds to the laser branding mark. E) shows an example of a polyadic synapse (a tetrad in this case), both as a scanning electron microscope image and as a diagram. F) shows a digital reconstruction of an olfactory sensory neuron based on the electron microscope images.

Highlighted results of the study

This study finds a number of interesting patterns that could lay the groundwork for future hypotheses. The authors distill their comparison of broadly tuned versus narrowly tuned glomeruli into five main findings:

  1. Narrowly tuned glomeruli have stronger feedforward olfactory sensory neuron connections to both uniglomerular projection neurons and multiglomerular neurons. That is to say, the input neurons have more connections to output neurons and interneurons.
  2. Narrowly tuned glomeruli have much stronger axo-axonic communication between sister olfactory sensory neurons. In other words, the input neurons have more possibilities to modulate each other’s input.
  3. Narrowly tuned glomeruli have stronger dendro-dendritic connections between uniglomerular projection neuron dendrites. The output neurons are better able to synchronize with each other and modulate each other.
  4. Narrowly tuned glomeruli have less feedback from uniglomerular projection neurons to multiglomerular neurons, a group that includes both multiglomerular projection neurons and local interneurons. The output neurons do not have as many opportunities to modulate the neurons that communicate between glomeruli.
  5. Narrowly tuned glomeruli have less feedback from multiglomerular neurons to olfactory sensory neurons. There are fewer opportunities for interneurons and multiglomerular projection neurons to modulate the input neurons, i.e., the inputs are subject to less “cross-talk” from other glomeruli.

These findings support the notion that narrowly tuned glomeruli establish more direct links with higher brain centers, with inputs being less influenced by other glomeruli. Conversely, broadly tuned glomeruli seem more receptive to signals from neighboring glomeruli. Narrowly tuned glomeruli exhibit greater intraglomerular modulation of inputs and outputs, potentially amplifying and synchronizing signal transmission.

One final finding that stands out: autapses, or synapses of a neuron onto itself, occurred at a greater frequency than the authors expected. This finding was especially true for the one uniglomerular projection neuron that leaves the broadly tuned DL5 glomerulus. It appears that the large, sprawling dendrite of this neuron synapses onto itself such that distant regions are more interconnected—perhaps providing the possibility for synchronizing the activity of the neuron.

Why I liked this preprint

As big, whole-brain connectomes become increasingly feasible, the methods used in this study for quickly making connectomes of important regions become especially interesting—they could make it possible to compare select regions across multiple individuals, to see just how representative one connectome can be. Moreover, because these methods link connectomics to functional work, it could be possible to compare between individuals that respond differently to a given stimulus and see how the physical connectivity of the brain influences different responses. Finally, this study provides a lot of interesting basic information about how important olfactory pathways are set up.

Questions for the authors

  1. In the study, you use the category “multiglomerular neurons” for both local interneurons and multiglomerular projection neurons. Could you elaborate on why you had to put these two disparate types of neurons in the same category, while you were able to differentiate olfactory sensory neurons and uniglomerular projection neurons? What additional steps would it take differentiate these?
  2. Do you have any reason to think that there might be similar patterns of differences in the multiglomerular projection neurons as with the uniglomerular projection neurons between broadly and narrowly tuned glomeruli?
  3. Do you think your method could complement whole connectomes of a single brain by comparing a region of interest in several individuals, or do you see this method as opening up other avenues of research?
  4. In your workflow, which parts of the process actually take the most time and are the main bottleneck? The tracing perhaps?
  5. In Figure 2 – figure supplement 1, it looks like there are some synapses without connections (no postsynaptic contacts per T-bar). Could you elaborate on that finding? Are those synapses in the process of forming/being pruned, or could they be the product of damaged brain slices?

Tags: fly, fruit fly, insect, neural circuits, neuronal architecture, olfaction, smell

Posted on: 16 July 2023 , updated on: 21 July 2023

doi: https://doi.org/10.1242/prelights.35133

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Author's response

Lydia Gruber and Jürgen Rybak shared

1. In the study, you use the category “multiglomerular neurons” for both local interneurons and multiglomerular projection neurons. Could you elaborate on why you had to put these two disparate types of neurons in the same category, while you were able to differentiate olfactory sensory neurons and uniglomerular projection neurons? What additional steps would it take differentiate these?

Our method does not allow us to differentiate between local interneurons (LNs) and multiglomerular projection neurons (mPNs) with absolute certainty because our analysis was performed in a restricted volume of neuropil. Therefore, the LN and mPN fibers could not be traced all the way to their respective cell bodies, which were outside of the defined volume. Within the glomerulus, the pattern and synaptic polarity of both cell types are too diverse to be categorized, i.e., there is no common feature that separates LNs from mPNs. To clearly differentiate between LNs and mPNs, a larger brain volume would be required, as seen in whole brain connectomes (e.g., Scheffer et al., 2020). Alternatively, a correlative approach utilizing transgenic cell markers in combination with focused ion beam-scanning electron microscopy (FIB-SEM) could be employed (as for example expression of peroxidase in specific cells as demonstrated by Rybak et al., 2016).

This contrasts with the olfactory sensory neurons and uniglomerular projection neurons, which can be distinguished by their morphology within the glomerulus. As it turns out, the synaptic inventory and the structure of the presynaptic density differ between the two groups and can be used to identify them, as shown in prior work (Rybak et al., 2016).

2. Do you have any reason to think that there might be similar patterns of differences in the multiglomerular projection neurons as with the uniglomerular projection neurons between broadly and narrowly tuned glomeruli?

To the best of our knowledge, the comprehensive understanding of the diverse group of multiglomerular projection neurons (mPNs) that globally innervate the antennal lobe is limited, both in terms of their connectivity and their classification into functional types—in contrast to the extensively studied uPNs (Strutz et al., 2014; Schlegel et.al, 2021). The restriction of uPN dendrites to one glomerulus, and their diverse functional contribution to the processing of the variety of odor information, clearly differentiates them from mPNs. Therefore, we can only speculate that the specific circuit features we have depicted for the uPN glomerular activity may not be relevant to the signal processing of mPNs.

3. Do you think your method could complement whole connectomes of a single brain by comparing a region of interest in several individuals, or do you see this method as opening up other avenues of research?

Indeed, our data could serve as a complement to the comprehensive maps, reported in two previous studies on whole brain connectomes in female Drosophila (Scheffer et al., 2020; Zheng et al., 2018). Thus, we could add up the synaptic inventory of glomerular circuits in the antennal lobe of a third female fly, thereby opening avenues to unraveling individual variability. The major advantage of our approach is to precisely define restricted volumes of neuropil for focused ion beam-scanning electron microscopy (FIB-SEM) imaging, thus avoiding the time-consuming generation of whole brain connectomes, which for most species is out of reach. For example, our method could facilitate imaging cellular features or defined cells across several individuals. One potential solution to accurately define cell types or cellular identities is by employing a correlative approach that incorporates cell type-specific tracers. FIB-SEM secondary electron imaging, when combined with fluorescence microscopy images, proves to be particularly suitable for such investigations (see, also response to question 1).

4. In your workflow, which parts of the process actually take the most time and are the main bottleneck? The tracing perhaps?

Indeed, segmentation (neurite tracing and annotating of synapses), along with training a team of experts, was the most time-consuming aspect of acquiring glomerular maps. The use of semi-automated segmentation tools could have accelerated the tracing process, but those tools are error-prone. In our study, we opted for purely manual segmentation due to the accuracy of human segmenters, eliminating the need for time-consuming proofreading. However, in future, we anticipate that the optimization of automated segmentation and pattern recognition tools will significantly alleviate this bottleneck.

5. In Figure 2 – figure supplement 1, it looks like there are some synapses without connections (no postsynaptic contacts per T-bar). Could you elaborate on that finding? Are those synapses in the process of forming/being pruned, or could they be the product of damaged brain slices?

This phenomenon cannot be attributed to brain slice damage, as our imaging technique involves acquiring FIB-SEM images rather than physical slices. Notably, all T-bars identified in our dataset exhibit a minimum of two postsynaptic contacts. The figure you referenced, which presents data on polyadicity, includes postsynaptic profiles that could not be conclusively identified or assigned to any of the three neuron classes. This is due to the fact that these profiles either originated from neurons of neighboring glomeruli or are very short neuronal fragments (less than 10 µm) known as “orphans.” Importantly, our comprehensive datasets do not provide evidence of synaptic assembly or disassembly.

References

Scheffer LK, Xu CS, Januszewski M, Lu Z, Takemura S-y, Hayworth KJ, et al. A connectome and analysis of the adult Drosophila central brain. eLife. 2020;9:e57443.

https://doi.org/10.7554/eLife.57443

Schlegel P, Bates AS, Sturner T, Jagannathan SR, Drummond N, Hsu J, et al. Information flow, cell types and stereotypy in a full olfactory connectome. eLife. 2021;10:e66018.

https://doi.org/10.7554/eLife.66018

Strutz A, Soelter J, Baschwitz A, Farhan A, Grabe V, Rybak J, et al. Decoding odor quality and intensity in the Drosophila brain. eLife. 2014; 3:e04147.

https://doi.org/10.7554/eLife.04147.001

Rybak J, Talarico G, Ruiz S, Arnold C, Cantera R, Hansson BS. Synaptic circuitry of identified neurons in the antennal lobe of Drosophila melanogaster. J Comp Neurol. 2016; 524(9):1920-56.

https://doi.org/10.1002/cne.23966

Zheng Z, Lauritzen JS, Perlman E, Robinson CG, Nichols M, Milkie D, et al. A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster. Cell. 2018;174(3):730-43.e22.

https://doi.org/10.1016/j.cell.2018.06.019

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