Invariant synaptic density across species
Curation statements for this article:-
Curated by eLife
eLife Assessment
This is a potentially valuable study in the area of connectomics, comparing across species to obtain a uniform synaptic density of ~1 synapse per micron. While the methods add to the field, the datasets are limited and filtered, and the role of cell-type on spacing is passed over. Additionally, some of the analyses could be tightened; hence, overall, the findings remain incomplete.
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (eLife)
Abstract
The nervous system scales with animal size while preserving function, yet the principles underlying this stability remain unclear. Here, we analyse the ultra-structure and connectivity of thousands of neuronal cells across species, including fly, zebrafish, mouse, and human, and found a conserved feature that stabilises neuronal responses across scales: an average of one synapse per micrometre of dendritic cable. We show that the appropriate synaptic density is shaped by correct axon-dendrite positioning and synaptic transmission during development. We find that this specific synaptic density is linked to wiring optimisation in neurons, where dendrites minimise cable length and conduction delays. Finally, simulations indicate invariant synaptic density as a neuronal design principle, conserved for its ability to synergise with other cell-intrinsic properties to stabilise voltage responses across cell types and species.
Article activity feed
-
eLife Assessment
This is a potentially valuable study in the area of connectomics, comparing across species to obtain a uniform synaptic density of ~1 synapse per micron. While the methods add to the field, the datasets are limited and filtered, and the role of cell-type on spacing is passed over. Additionally, some of the analyses could be tightened; hence, overall, the findings remain incomplete.
-
Joint Public Review:
Summary:
Brain sizes vary by orders of magnitude between different organisms, while neuronal circuits maintain essential computational functions. Castro and Cardona explore invariant features across animal species that lend themselves as fundamental constraints on scaling mechanisms. The authors leverage a number of existing connectomics datasets for their study, from which they extract morphologies and synaptic connections for numerous neurons, resulting in a set of neuronal features such as neuron lengths and synapse counts.
Their core result is a rather stable synapse density of 1 synapse per 1 µm of dendritic path length across neurons from different datasets, in line with prior results in mouse (Turner et al., 2022) and human (Lomba et al., 2022). The authors show that this finding also holds for the selected …
Joint Public Review:
Summary:
Brain sizes vary by orders of magnitude between different organisms, while neuronal circuits maintain essential computational functions. Castro and Cardona explore invariant features across animal species that lend themselves as fundamental constraints on scaling mechanisms. The authors leverage a number of existing connectomics datasets for their study, from which they extract morphologies and synaptic connections for numerous neurons, resulting in a set of neuronal features such as neuron lengths and synapse counts.
Their core result is a rather stable synapse density of 1 synapse per 1 µm of dendritic path length across neurons from different datasets, in line with prior results in mouse (Turner et al., 2022) and human (Lomba et al., 2022). The authors show that this finding also holds for the selected neurons in Drosophila (adult and larva) and zebrafish larva, and further aim to explore mechanisms creating this density and its consequences on neuronal excitability. They show that their data is in agreement with previously reported scaling rules of dendritic arborization (Cuntz et al., 2012). Through exploration of synapse sizes and dendrite radii, the authors identify a compensatory effect of dendrite radius for fluctuations of synaptic densities that can provide voltage response stabilities.
Overall, this study is an addition to the burgeoning field of comparative connectomics, and as such, the presented analyses are important methodological contributions. The conclusions presented here are, however, too general. The analyzed datasets are reduced to a few cell types due to technical challenges, and cell type identities are ignored even though they have large ramifications for the analyses presented (e.g., E vs I for stability analyses, and cell type correlations with synapse sizes).
Strengths:
(1) Novelty.
The authors provide new ways to compare neuronal measurements between brains and species and how to interpret the results. In particular, their conclusions for normalization of connection strength (by total number of synapses onto a neuron) are an important contribution to the analysis of neuronal circuits, especially in Drosophila. The finding of the density is important, but similar numbers have been reported by prior studies of some of the same datasets (Loomba, Turner).
(2) Wiring optimization.
The presented study is an important validation of the scaling law across organisms and cell types.
(3) Scale.
The authors collected neurons from several connectomics datasets from different organisms that were created by different labs, requiring standardizing reconstructions and extracting features across a large number of neurons
Weaknesses:
(1) Data preparation and presentation. The authors describe how the data was processed and manually corrected in the methods, but at no point other than in Figure S4 (which shows a single neuron from afar) is an actual neuron skeleton shown to convince the reader that this was done well. The resolution of the skeleton has important ramifications for the measured path lengths. Further, it is not clear how spine heads, very short branches, and twigs were handled. Tracing out spine heads could double the path length, similar for twigs. How these were handled should be discussed and justified. Radius measurements were taken at the trunk of the neuron, but for synapses on spines, the spine size (especially neck (Harnett et al., 2012)) is critical for estimating input resistance. This should at least be discussed and justified. Further, it is not clear how synapses onto the soma were handled. What was chosen as the radius there?
(2) Data filtering. The authors excluded substantial numbers of neurons from the analyzed datasets, leaving only cells from a few cell types. However, the manuscript gives the impression in many places that the analyses were run across the entirety of those datasets. Given the number of neurons used (e.g. only 60 from the Drosophila adult!), it would be much more prudent to talk about the specific cell types and not the datasets/animals. Further, the way the included neurons were chosen is likely to introduce a bias. E.g., in Drosophila datasets, neurons with more synapses on the backbone rather than twigs automatically have higher postsynaptic completion ratios. Overall, given the number of neurons, the results cannot be easily generalized even for the datasets used here.
(3) Ignoring cell types. Especially for the mammalian datasets, presynaptic cell type identity is correlated with synapse size and location. Ignoring this information removes important context from the analysis. For instance, inhibitory synapses are more prevalent close to the soma, etc.. This clearly impacts the analyses presented, yet is ignored and not discussed. Further, the sign of the synapses and, in particular, the balance of E vs I synapses, is important for the analysis of voltage response stability.
(4) Wiring optimization rule. The presented analysis convincingly confirms the scaling law from Cuntz, et al. (Figure 3b). However, taken together with the prior finding of the 1 synapse / 1 µm density along dendrites, the sensitivity analysis appears to be circular (Fig. 3c). Since the inputs are length and synapse count, the same that were put into the density analysis, the result rather reconfirms the scaling law.
(5) Synapse size and PSD area. Two measurements for the synapses were used for the MICrONS datasets, but in some places, this is confusing. Why were the volumetric synapse sizes not fully replaced with the PSD measurements, as these are likely a superior measurement of the "size" of the synapse? Annotations of the PSD should also be shown somewhere to convince the reader of their quality.
(6) Zebrafish dataset. The validity of the corrections used for the zebrafish dataset is difficult to judge for someone not familiar with that dataset. It appears that there are substantial problems in the reconstruction of the dendritic tips. Other datasets were ignored for seemingly similar reasons. Why was this one kept? The correction factor introduces an arbitrariness to the analysis, and it directly affects the core result of this study.
-
