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

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dc.contributor.authorYoon, Young-Gyuko
dc.contributor.authorDai, Peilunko
dc.contributor.authorWohlwend, Jeremyko
dc.contributor.authorChang, Jae-Byumko
dc.contributor.authorMarblestone, Adam H.ko
dc.contributor.authorBoyden, Edward S.ko
dc.date.accessioned2018-09-18T06:24:47Z-
dc.date.available2018-09-18T06:24:47Z-
dc.date.created2018-09-04-
dc.date.created2018-09-04-
dc.date.created2018-09-04-
dc.date.created2018-09-04-
dc.date.issued2017-10-
dc.identifier.citationFRONTIERS IN COMPUTATIONAL NEUROSCIENCE, v.11-
dc.identifier.issn1662-5188-
dc.identifier.urihttp://hdl.handle.net/10203/245587-
dc.description.abstractWe 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.-
dc.languageEnglish-
dc.publisherFRONTIERS MEDIA SA-
dc.titleFeasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and Barcode-Guided Agglomeration-
dc.typeArticle-
dc.identifier.wosid000413539600001-
dc.type.rimsART-
dc.citation.volume11-
dc.citation.publicationnameFRONTIERS IN COMPUTATIONAL NEUROSCIENCE-
dc.identifier.doi10.3389/fncom.2017.00097-
dc.contributor.localauthorYoon, Young-Gyu-
dc.contributor.localauthorChang, Jae-Byum-
dc.contributor.nonIdAuthorDai, Peilun-
dc.contributor.nonIdAuthorWohlwend, Jeremy-
dc.contributor.nonIdAuthorMarblestone, Adam H.-
dc.contributor.nonIdAuthorBoyden, Edward S.-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
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