Ground truth data used to train the synapse classifier used in Lillvis et al., 2022 for ExLLSM circuit reconstruction

Brain function is mediated by the physiological coordination of a vast,
intricately connected network of molecular and cellular components. The
physiological properties of network components can be quantified with high
throughput; the ability to assess many animals per study has been key to
relating physiological properties to behavior. Conversely, detailed
anatomical properties (e.g., the synaptic connectivity of
molecularly-defined cell types across an entire circuit) are presently
quantifiable only with low throughput; thus we know very little about how
network structure, and structural variation, influences behavior. For
neuroanatomical reconstruction there is a methodological gulf between
electron-microscopic (EM) methods, which yield dense connectomes (but at
great expense and low throughput) and light-microscopic methods, which
provide molecular and cell-type specificity with high throughput (but
without synaptic resolution). 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. Here, we share the data used to train the synapse
classifier that was utilized in the analysis pipeline. All additional
software, code, and usage examples to train and run the classifier can be
found at Github:
https://github.com/JaneliaSciComp/exllsm-circuit-reconstruction