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Automatic detection of synaptic partners in a whole-brain Drosophila electron microscopy data set

Abstract

We develop an automatic method for synaptic partner identification in insect brains and use it to predict synaptic partners in a whole-brain electron microscopy dataset of the fruit fly. The predictions can be used to infer a connectivity graph with high accuracy, thus allowing fast identification of neural pathways. To facilitate circuit reconstruction using our results, we develop CIRCUITMAP, a user interface add-on for the circuit annotation tool CATMAID.

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Fig. 1: Overview of method for synaptic partner prediction, application to the FAFB dataset and use of CIRCUITMAP for circuit reconstruction and analysis in CATMAID.
Fig. 2: Results in whole-brain FAFB dataset.

Data availability

The datasets generated and/or analysed during the current study are available in the ‘synful_fafb’ repository20 and in ‘synful_experiments’21. The following figures and tables use this data.

Code availability

The code used to train networks and predict synaptic partners is available in the synful repository (https://github.com/funkelab/synful)22. Code used to evaluate the results is available in the synful_fafb repository (https://github.com/funkelab/synful_fafb)20. A python library to query circuits in the FAFB volume using the predicted synaptic partners is available in the SynfulCircuit repository (https://github.com/funkelab/synfulcircuit)14. The CATMAID widget used to interactively query circuits is available in the CircuitMap repository (https://github.com/catmaid/circuitmap)15.

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Acknowledgements

We thank S. Lauritzen (HHMI, Janelia Research Campus) for helping with data acquisition; M. Nichols (HHMI, Janelia Research Campus) for importing CATMAID annotations; W. Patton (HHMI, Janelia Research Campus) for code contributions; N. Eckstein (HHMI, Janelia Research Campus) for helpful discussions; J. Maitin-Shepard (Google) for adding synapse visualization features to neuroglancer; V. Jayaraman (HHMI, Janelia Research Campus) for providing evaluation data; and the HHMI Janelia Connectome Annotation Team (R. Parekh, A. Suleiman, T. Paterson) for evaluation data. We also thank Z. Zheng, F. Li, C. Fisher, N. Sharifi and S. Calle-Schuler (HHMI, Janelia) for access to prepublication data used for evaluation. This work was supported by the HHMI and the Swiss National Science Foundation (grant 205321L 160133).

Author information

Authors and Affiliations

Authors

Contributions

S.C.T. and J.F. conceived the work. W.-C.A.L., R.W., M.C. and J.F. acquired funding. J.B., A.S., C.M.-M., S.G., R.K., T.M.N., L.H., P.S., S.S. and J.F. developed software. J.B., C.M.-M. and J.F. carried out validation and evaluation. P.S., G.S.X.E.J. and D.D.B. generated evaluation data. J.B., T.K., S.G. and J.F. disseminated data. M.C. and J.K. supervised the work. J.B., A.S., C.M.-M., P.S. and J.F. produced visualizations. J.B., P.S. and J.F. drafted the manuscript. All authors reviewed and edited the manuscript.

Corresponding author

Correspondence to Jan Funke.

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Competing interests

S.G. is the founder and CEO of UniDesign Solutions GmbH, which provides IT services.

Additional information

Peer review informationNature Methods thanks Chung-Chuan Lo, Stephan Sigrist and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Brain-wide prediction accuracy.

Precision and recall over 10 densely annotated cubes in different brain regions (dataset DenseCubes). Highlights show best f-score over the detection threshold. Top row: Black triangle marks show the inter-human variance between two annotations, with one annotation treated as ground-truth evaluated against the other one. Bottom right: Visualization of the cube locations within the FAFB dataset.

Source data

Extended Data Fig. 2 Polarity of olfactory projection neurons (PN).

a, Outline of axon-dendrite splitting procedure. b, Exemplary well segregated (left) and unsegregated (right) PN. Boxplot shows segregation index (SI) for all PNs separated into uni- and multiglomerular PNs. c, Impact of completeness of neuronal reconstruction on recovery of synapses.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–3, Notes 1–3 and Discussion

Reporting Summary

Supplementary Data 1

Per-neuron prediction accuracy

Supplementary Video 1

CircuitMap instruction video

Supplementary Data 2

Source data for Supplementary Fig. 2.

Source data

Source Data Fig. 2

Source data for Fig. 2

Source Data Extended Data Fig. 1

Source data for Extended Data Fig. 1

Source Data Extended Data Fig. 2

Source data for Extended Data Fig. 2

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Buhmann, J., Sheridan, A., Malin-Mayor, C. et al. Automatic detection of synaptic partners in a whole-brain Drosophila electron microscopy data set. Nat Methods 18, 771–774 (2021). https://doi.org/10.1038/s41592-021-01183-7

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