Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and Barcode-Guided Agglomeration

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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, aswell as algorithms, to ultimately enable routine, high-performance optical circuit reconstruction.
Publisher
FRONTIERS MEDIA SA
Issue Date
2017-10
Language
English
Article Type
Article
Citation

FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, v.11

ISSN
1662-5188
DOI
10.3389/fncom.2017.00097
URI
http://hdl.handle.net/10203/245587
Appears in Collection
EE-Journal Papers(저널논문)MS-Journal Papers(저널논문)
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