Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and Barcode-Guided Agglomeration
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 m...
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Frontiers Research Foundation
2018
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| Online Access: | http://hdl.handle.net/1721.1/115370 https://orcid.org/0000-0003-1812-6421 https://orcid.org/0000-0002-1680-0526 https://orcid.org/0000-0003-2055-4900 https://orcid.org/0000-0002-0419-3351 |
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| author | Yoon, Young Gyu Dai, Peilun Wohlwend, Jeremy Chang, Jae-Byum Marblestone, Adam Henry Boyden, Edward |
| author2 | Massachusetts Institute of Technology. Department of Biological Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Biological Engineering Yoon, Young Gyu Dai, Peilun Wohlwend, Jeremy Chang, Jae-Byum Marblestone, Adam Henry Boyden, Edward |
| author_sort | Yoon, Young Gyu |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T14:39:00Z |
| format | Article |
| id | mit-1721.1/115370 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T14:39:00Z |
| publishDate | 2018 |
| publisher | Frontiers Research Foundation |
| record_format | dspace |
| spelling | mit-1721.1/1153702022-10-01T21:56:20Z Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and Barcode-Guided Agglomeration Yoon, Young Gyu Dai, Peilun Wohlwend, Jeremy Chang, Jae-Byum Marblestone, Adam Henry Boyden, Edward 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 Yoon, Young Gyu Dai, Peilun Wohlwend, Jeremy Chang, Jae-Byum Marblestone, Adam Henry Boyden, Edward 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. National Institutes of Health (U.S.) (Grant 1R41MH112318) National Institutes of Health (U.S.) (Grant 1R01MH110932) United States. Army Research Office (Grant W911NF1510548) National Institutes of Health (U.S.) (Grant 1RM1HG008525) National Institutes of Health (U.S.) (Award 1DP1NS087724) 2018-05-14T19:34:32Z 2018-05-14T19:34:32Z 2017-10 2017-08 2018-05-04T13:44:32Z Article http://purl.org/eprint/type/JournalArticle 1662-5188 http://hdl.handle.net/1721.1/115370 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. https://orcid.org/0000-0003-1812-6421 https://orcid.org/0000-0002-1680-0526 https://orcid.org/0000-0003-2055-4900 https://orcid.org/0000-0002-0419-3351 http://dx.doi.org/10.3389/FNCOM.2017.00097 Frontiers in Computational Neuroscience Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/ application/pdf Frontiers Research Foundation Frontiers |
| spellingShingle | Yoon, Young Gyu Dai, Peilun Wohlwend, Jeremy Chang, Jae-Byum Marblestone, Adam Henry Boyden, Edward Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and Barcode-Guided Agglomeration |
| title | Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and Barcode-Guided Agglomeration |
| title_full | Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and Barcode-Guided Agglomeration |
| title_fullStr | Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and Barcode-Guided Agglomeration |
| title_full_unstemmed | Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and Barcode-Guided Agglomeration |
| title_short | Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and Barcode-Guided Agglomeration |
| title_sort | feasibility of 3d reconstruction of neural morphology using expansion microscopy and barcode guided agglomeration |
| url | http://hdl.handle.net/1721.1/115370 https://orcid.org/0000-0003-1812-6421 https://orcid.org/0000-0002-1680-0526 https://orcid.org/0000-0003-2055-4900 https://orcid.org/0000-0002-0419-3351 |
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