Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster

Nils Eckstein, Alexander Shakeel Bates, Andrew Champion, Michelle Du, Yijie Yin, Philipp Schlegel, Alicia Kun Yang Lu, Thomson Rymer, Samantha Finley-May, Tyler Paterson, Ruchi Parekh, Sven Dorkenwald, Arie Matsliah, Szi Chieh Yu, Claire McKellar, Amy Sterling, Katharina Eichler, Marta Costa, Sebastian Seung, Mala MurthyVolker Hartenstein, Gregory S.X.E. Jefferis, Jan Funke

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

High-resolution electron microscopy of nervous systems has enabled the reconstruction of synaptic connectomes. However, we do not know the synaptic sign for each connection (i.e., whether a connection is excitatory or inhibitory), which is implied by the released transmitter. We demonstrate that artificial neural networks can predict transmitter types for presynapses from electron micrographs: a network trained to predict six transmitters (acetylcholine, glutamate, GABA, serotonin, dopamine, octopamine) achieves an accuracy of 87% for individual synapses, 94% for neurons, and 91% for known cell types across a D. melanogaster whole brain. We visualize the ultrastructural features used for prediction, discovering subtle but significant differences between transmitter phenotypes. We also analyze transmitter distributions across the brain and find that neurons that develop together largely express only one fast-acting transmitter (acetylcholine, glutamate, or GABA). We hope that our publicly available predictions act as an accelerant for neuroscientific hypothesis generation for the fly.

Original languageEnglish (US)
Pages (from-to)2574-2594.e23
JournalCell
Volume187
Issue number10
DOIs
StatePublished - May 9 2024

All Science Journal Classification (ASJC) codes

  • General Biochemistry, Genetics and Molecular Biology

Keywords

  • neuroscience, machine learning, electron microscopy, Drosophila melanogaster, neurotransmitter, explainable AI

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