Rapid reconstruction of neural circuits using tissue expansion and light sheet microscopy
Brain function is mediated by the physiological coordination of a vast, intricately connected network of molecular and cellular components. The physiological properties of neural network components can be quantified with high throughput. The ability to assess many animals per study has been critical in relating physiological properties to behavior. By contrast, the synaptic structure of neural circuits is presently quantifiable only with low throughput. This low throughput hampers efforts to understand how variations in network structure relate to variations in behavior. For neuroanatomical reconstruction there is a methodological gulf between electron-microscopic (EM) methods, which yield dense connectomes at considerable expense and low throughput, and light-microscopic (LM) methods, which provide molecular and cell-type specificity at high throughput but without synaptic resolution. To bridge this gulf, we developed a high-throughput analysis pipeline and imaging protocol using tissue expansion and light sheet microscopy (ExLLSM) to rapidly reconstruct selected circuits across many animals with single-synapse resolution and molecular contrast. Using Drosophila to validate this approach, we demonstrate that it yields synaptic counts similar to those obtained by EM, enables synaptic connectivity to be compared across sex and experience, and can be used to correlate structural connectivity, functional connectivity, and behavior. This approach fills a critical methodological gap in studying variability in the structure and function of neural circuits across individuals within and between species.
All software and code used for data analysis is available at Github (https://github.com/JaneliaSciComp/exllsm-circuit-reconstruction). Ground truth data used to train the synapse classifier is available at Dryad (https://doi.org/10.5061/dryad.5hqbzkh8b). All genetic reagents are available upon request. The data used to generate the figures and videos in this manuscript exceeds 100TB. Therefore, it is not practical to upload the data to a public repository. However, all data used in this paper will be made freely available to those who request and provide a mechanism for feasible data transfers (physical hard drives, cloud storage, etc.). Documentation for construction of a lattice light-sheet microscope can be obtained by execution of a research license agreement with HHMI.
Ground truth data used to train the synapse classifier used in Lillvis et al., 2022 for ExLLSM circuit reconstructionDryad Digital Repository, doi:10.5061/dryad.5hqbzkh8b.
Article and author information
Howard Hughes Medical Institute
- Barry J Dickson
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Matthieu Louis, University of California, Santa Barbara, United States
- Preprint posted: November 15, 2021 (view preprint)
- Received: June 20, 2022
- Accepted: October 25, 2022
- Accepted Manuscript published: October 26, 2022 (version 1)
- Version of Record published: November 11, 2022 (version 2)
© 2022, Lillvis et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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