Abstract
Neurons emit axons, which form synapses, the fundamental unit of the nervous system. Neuroscientists use genetic anterograde tracing methods to label the synaptic output of specific neuronal subpopulations, but the resulting data sets are too large for manual analysis, and current automated methods have significant limitations in cost and quality. In this paper, we describe a pipeline optimized to identify anterogradely labeled presynaptic boutons in brain tissue sections. Our histologic pipeline labels boutons with high sensitivity and low background. To automatically detect labeled boutons in slide-scanned tissue sections, we developed BoutonNet. This detector uses a two-step approach: an intensity-based method proposes possible boutons, which are checked by a neural network-based confirmation step. BoutonNet was compared to expert annotation on a separate validation data set and achieved a result within human inter-rater variance. This open-source technique will allow quantitative analysis of the fundamental unit of the brain on a whole-brain scale.
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Data availability
Code for retraining and using BoutonNet is available under the Gnu General Public License v3 at a public repository: https://github.com/GeerlingLab/BoutonCount. Code used for calculating Dice coefficients to compare manual and automatic counts of boutons is available on reasonable request. Full-resolution images of training and testing data samples, as well as slide-scanned VSI files from which these samples are derived are available upon reasonable request.
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Acknowledgements
We thank Alison Hsu for her assistance in counting presynaptic boutons and Patrick Grady for his assistance with designing neural networks.
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This work was supported by the National Institute of Neurological Disorders and Stroke (K08 grant NS099425 to JCG). The authors have no relevant financial or non-financial interests to disclose.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by FG. Boutons were counted independently by all authors. The first draft of the manuscript was written by FG and JG, and all authors commented on following versions of the manuscript. All authors read and approved the final manuscript.
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All procedures performed in studies involving animals were in accordance with the ethical standards of the University of Iowa and were approved by the University of Iowa Institutional Animal Care and Use Committee.
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Grady, F.S., Graff, S.A., Aldridge, G.M. et al. BoutonNet: an automatic method to detect anterogradely labeled presynaptic boutons in brain tissue sections. Brain Struct Funct 227, 1921–1932 (2022). https://doi.org/10.1007/s00429-022-02504-y
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DOI: https://doi.org/10.1007/s00429-022-02504-y