Quantitative analysis of rabies virus-based synaptic connectivity tracing
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Tran-Van-Minh et al., attempt to develop a statistical approach which will allow consolidation of new, as well as previously-acquired datasets, to yield biologically significant insights into the logic underlying rabies vectors' expansion from single starter cells. While such work is called for, many of the premises presented here will need to be significantly adjusted, before the approach could be put into widespread use.
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Abstract
Monosynaptically restricted rabies viruses have been used for more than a decade for synaptic connectivity tracing. However, the verisimilitude of quantitative conclusions drawn from these experiments is largely unknown. The primary reason is the simple metrics commonly used, which generally disregard the effect of starter cell numbers. Here we present an experimental dataset with a broad range of starter cell numbers and explore their relationship with the number of input cells across the brain using descriptive statistics and modelling. We show that starter cell numbers strongly affect input fraction and convergence index measures, making quantitative comparisons unreliable. Furthermore, we suggest a principled way to analyse rabies derived connectivity data by taking advantage of the starter vs input cell relationship that we describe and validate across independent datasets.
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eLife assessment
Tran-Van-Minh et al., attempt to develop a statistical approach which will allow consolidation of new, as well as previously-acquired datasets, to yield biologically significant insights into the logic underlying rabies vectors' expansion from single starter cells. While such work is called for, many of the premises presented here will need to be significantly adjusted, before the approach could be put into widespread use.
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Reviewer #1 (Public Review):
Modified rabies viral vectors allow high throughput mapping of neuronal circuits with cell type specificity. However, lack of standardization in this field limits extrapolation of useful information, beyond the identity of anatomically connected regions, such as differential input densities and connectivity motifs. in this manuscript, Tran-Van-Minh et al., attempt to develop a statistical approach which will allow consolidation of new, as well as previously-acquired datasets, to yield biologically significant insights into the logic underlying rabies vectors' expansion from single starter cells.
This question the authors address is of high importance and the presentation of this manuscript is timely, as rabies tracing experiments increase at an exponential rate. The authors provide a largely complete …
Reviewer #1 (Public Review):
Modified rabies viral vectors allow high throughput mapping of neuronal circuits with cell type specificity. However, lack of standardization in this field limits extrapolation of useful information, beyond the identity of anatomically connected regions, such as differential input densities and connectivity motifs. in this manuscript, Tran-Van-Minh et al., attempt to develop a statistical approach which will allow consolidation of new, as well as previously-acquired datasets, to yield biologically significant insights into the logic underlying rabies vectors' expansion from single starter cells.
This question the authors address is of high importance and the presentation of this manuscript is timely, as rabies tracing experiments increase at an exponential rate. The authors provide a largely complete description of the main pitfalls and caveats in current analysis approaches and common misconceptions in the interpretation of results. In addition, they correctly diagnose the potential of this methodology to extract pertinent information from such experiments and provide the reader with useful tips on how to better design, analyze and interpret them.
While such a paper is undoubtedly called for, and has the potential to substantially improve circuit mapping experiments, with little cost to the experimenter, there are a few critical flaws in their premises, which will limit a widespread adoption of their analysis approach. While the authors discuss caveats in design and interpretation of results, they do not implement these suggestions into their own experiments, such as the need for accurate estimation of starter cells and automated cell counting which is not biased by strong signal coming from dendritic arbors/axons in densely-labeled regions. While the authors claim that the reduction in residuals and relative conformity across experiments following log-transformation of n(i) and n(s) shows that their approach is robust, the large degree of variability across experiments, the datasets of some are biologically implausible, showing a decrease in n(i) as a function of n(s), suggests that this transformation disposes of useful information, which might help detect anomalies in data acquisition. The fact that the most rigorously-acquired dataset which was presented by the Allen Institute (BRAIN Initiative Cell Census Network, Nature, 2021) is the only one which, does not comply with the transformation, attests further to this caveat. This is probably the reason why the estimated number of individual neurons retrogradely labeled by a single starter cell (~1400) is more than an order of magnitude higher than any previously-published estimation, including those which include tracing from single-cell, and is highly unrealistic.
In addition, the model selected by the authors to fit the various datasets does not take into proper account saturation of n(i), as all proposed functions are growing functions. Here, the most suitable model which should be used to describe the nature of RV expansion is a cumulative distribution function, while the rest are either private cases (e.g. linear fit given low n(s) or zero divergence) or are biologically unrealistic (e.g. exponential fit). Again here, the only dataset which appears to fit this function the best comes from the BRAIN Initiative Cell Census Network (Nature, 2021).
In conclusion, while such work is called for and has the potential of becoming a staple in the connectivity toolbox, many of the premises presented here will need to be significantly adjusted, before the approach could be put into widespread use.
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Reviewer #2 (Public Review):
Appropriate brains functions require the precise wiring of vast numbers of synaptic connections in broadly distributed neural circuits. Monosynaptic retrograde rabies virus tracing has become a common approach in neuroscience to assay presynaptic inputs into a given postsynaptic region. However, quantification and interpretation of rabies tracing data is confounded by the lack of uniform and appropriate measuring approaches across different studies and laboratories, as well as the lack of knowledge of the trans-synaptic transfer properties of different rabies viruses in various brain regions.
The current study comprehensively applies mathematical approaches to an example rabies tracing dataset in layer 5 of mouse visual cortex, as well as previously published datasets, to propose more standardized …
Reviewer #2 (Public Review):
Appropriate brains functions require the precise wiring of vast numbers of synaptic connections in broadly distributed neural circuits. Monosynaptic retrograde rabies virus tracing has become a common approach in neuroscience to assay presynaptic inputs into a given postsynaptic region. However, quantification and interpretation of rabies tracing data is confounded by the lack of uniform and appropriate measuring approaches across different studies and laboratories, as well as the lack of knowledge of the trans-synaptic transfer properties of different rabies viruses in various brain regions.
The current study comprehensively applies mathematical approaches to an example rabies tracing dataset in layer 5 of mouse visual cortex, as well as previously published datasets, to propose more standardized methodologies for rabies data analysis and interpretation. The major strength of the study is the rigorous and unbiased mathematical approaches applied to their data and a range of previously published studies in the field. Inclusion of representative image data would be helpful for readers and would further strengthen the study. Given the ubiquitous use of rabies virus tracing in the field, yet lack of insight into this crucial aspect of its use, this will provide a useful resource for the neuroscience community.
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