Rapid reconstruction of neural circuits using tissue expansion and light sheet microscopy

Lillvis JL, Otsuna H, Ding X, Pisarev I, Kawase T, Colonell J, Rokicki K, Goina C, Gao R, Hu A, Wang K, Bogovic J, Milkie DE, Meienberg L, Mensh BD, Boyden ES, Saalfeld S, Tillberg PW, Dickson BJ (2022) Rapid reconstruction of neural circuits using tissue expansion and light sheet microscopy, Elife 11:e81248.

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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.


Understanding normal and pathological brain computations

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