Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and Barcode-Guided Agglomeration
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fncom-11-00097.pdf
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3.73 MB
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Checksum (MD5)
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Author(s)
Yoon, Young Gyu
Dai, Peilun
Wohlwend, Jeremy
Chang, Jae-Byum
Marblestone, Adam Henry
Boyden, Edward
Date Issued
October 2017
Journal
Frontiers in Computational Neuroscience
Publisher
Frontiers Research Foundation
Citation
Yoon, Young-Gyu et al. “Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and Barcode-Guided Agglomeration.” Frontiers in Computational Neuroscience 11 (October 2017): 97 © 2017 Yoon et al.
Version
Final published version
Abstract
We here introduce and study the properties, via computer simulation, of a candidate automated approach to algorithmic reconstruction of dense neural morphology, based on simulated data of the kind that would be obtained via two emerging molecular technologies—expansion microscopy (ExM) and in-situ molecular barcoding. We utilize a convolutional neural network to detect neuronal boundaries from protein-tagged plasma membrane images obtained via ExM, as well as a subsequent supervoxel-merging pipeline guided by optical readout of information-rich, cell-specific nucleic acid barcodes. We attempt to use conservative imaging and labeling parameters, with the goal of establishing a baseline case that points to the potential feasibility of optical circuit reconstruction, leaving open the possibility of higher-performance labeling technologies and algorithms. We find that, even with these conservative assumptions, an all-optical approach to dense neural morphology reconstruction may be possible via the proposed algorithmic framework. Future work should explore both the design-space of chemical labels and barcodes, as well as algorithms, to ultimately enable routine, high-performance optical circuit reconstruction.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Media Laboratory
McGovern Institute for Brain Research at MIT
Terms of Use
Attribution 4.0 International (CC BY 4.0)
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DOI of Published Version
https://doi.org/10.3389/FNCOM.2017.00097
