A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster

By Zhihao Zheng, J. Lauritzen, E. Perlman, Camenzind G. Robinson, Matthew Nichols, D. Milkie, O. Torrens, J. Price, Corey B. Fisher, Nadiya Sharifi and 13 more. 2017

901 citations recorded by the source corpus.

Drosophila melanogaster has a rich repertoire of innate and learned behaviors. Its 100,000–neuron brain is a large but tractable target for comprehensive neural circuit mapping. Only electron microscopy (EM) enables complete, unbiased mapping of synaptic connectivity; however, the fly brain is too large for conventional EM. We developed a custom high-throughput EM platform and imaged the entire brain of an adult female fly. We validated the dataset by tracing brain-spanning circuitry involving the mushroom body (MB), intensively studied for its role in learning. Here we describe the complete set of olfactory inputs to the MB; find a new cell type providing driving input to Kenyon cells (the intrinsic MB neurons); identify neurons postsynaptic to Kenyon cell dendrites; and find that axonal arbors providing input to the MB calyx are more tightly clustered than previously indicated by light-level data. This freely available EM dataset will significantly accelerate Drosophila neuroscience. HIGHLIGHTS - A complete adult fruit fly brain was imaged, using electron microscopy (EM) - The EM volume enables brain-spanning mapping of neuronal circuits at the synaptic level - Olfactory projection neurons cluster more tightly in mushroom body calyx than expected from light-level data - The primary postsynaptic targets of Kenyon cells (KCs) in the MB are other KCs, as well as the anterior paired lateral (APL) neuron - A newly discovered cell type, MB-CP2, integrates input from several sensory modalities and provides microglomerular input to KCs in MB calyx - A software pipeline was created in which EM-traced skeletons can be searched for within existing large-scale light microscopy (LM) databases of neuronal morphology, facilitating cell type identification and discovery of relevant genetic driver lines

Subfields: Aging, Cellular and Molecular Neuroscience, Genetics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aging

1. Gerald M. Rubin
2. Andrew C. Lin

In Cellular and Molecular Neuroscience

1. Andrew C. Lin
2. Hoger Amin
3. V. Jayaraman
4. Gerald M. Rubin
5. Yoshinori Aso
6. Glenn C. Turner
7. S. Waddell

In Genetics

1. Andrew C. Lin
2. Hoger Amin
3. V. Jayaraman
4. Gerald M. Rubin
5. Yoshinori Aso

Citations in the corpus

• Re-evaluating circuit mechanisms underlying pattern separation (2019, neutral): “Projection neurons integrate signals from stereotyped sets of glomeruli, which form random synaptic connections onto Kenyon cells in the mushroom body calyx (Figure 2B) (Masuda-Nakagawa et al., 2005; Murthy et al., 2008; Caron et al., 2013; but see Eichler et al., 2017; Zheng et al., 2018).”
• Recognition (2003, positive): “Individual KCs receive unstructured input from ∼ 4 − 10 PNs (Caron et al., 2013; Li et al., 2020; Zheng et al., 2018), and piriform neurons receive roughly 30 − 100 inputs from a random collection of glomeruli (Davison & Ehlers, 2011; Miyamichi et al., 2011).”
• A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence (2021, background): “and MNI (Supplementary Video 4) group spaces; movies that inspect raw and pre-processed EPI volumes (Supplementary Video 5); and movies that provide volume and surface visualizations of the stability of mean EPI intensity across sessions (Supplementary Videos 6 and 7 and Supplementary Fig.”
• A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection (2020, methodology): “However, this study, which was based primarily on the Full Adult Fly Brain (FAFB) dataset (Zheng et al., 2018), relied on manual reconstruction of only a fraction of the circuit.”
• Neuronal mechanisms underlying innate and learned olfactory processing in Drosophila. (2019, background): “This connectivity is not stereotyped [58,59], but is 178 also not purely stochastic, as PNs with similar odor tuning profiles, and from the same 179 glomeruli, tend to converge on the same KCs [57,60].”