Toward a more accurate 3D atlas of C. elegans neurons
Abstract Background Determining cell identity in volumetric images of tagged neuronal nuclei is an ongoing challenge in contemporary neuroscience. Frequently, cell identity is determined by aligning and matching tags to an “atlas” of labeled neuronal...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BioMed Central
2022
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| Online Access: | https://hdl.handle.net/1721.1/142854 |
| _version_ | 1826195538219892736 |
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| author | Skuhersky, Michael Wu, Tailin Yemini, Eviatar Nejatbakhsh, Amin Boyden, Edward Tegmark, Max |
| author_facet | Skuhersky, Michael Wu, Tailin Yemini, Eviatar Nejatbakhsh, Amin Boyden, Edward Tegmark, Max |
| author_sort | Skuhersky, Michael |
| collection | MIT |
| description | Abstract
Background
Determining cell identity in volumetric images of tagged neuronal nuclei is an ongoing challenge in contemporary neuroscience. Frequently, cell identity is determined by aligning and matching tags to an “atlas” of labeled neuronal positions and other identifying characteristics. Previous analyses of such C. elegans datasets have been hampered by the limited accuracy of such atlases, especially for neurons present in the ventral nerve cord, and also by time-consuming manual elements of the alignment process.
Results
We present a novel automated alignment method for sparse and incomplete point clouds of the sort resulting from typical C. elegans fluorescence microscopy datasets. This method involves a tunable learning parameter and a kernel that enforces biologically realistic deformation. We also present a pipeline for creating alignment atlases from datasets of the recently developed NeuroPAL transgene. In combination, these advances allow us to label neurons in volumetric images with confidence much higher than previous methods.
Conclusions
We release, to the best of our knowledge, the most complete full-body C. elegans 3D positional neuron atlas, incorporating positional variability derived from at least 7 animals per neuron, for the purposes of cell-type identity prediction for myriad applications (e.g., imaging neuronal activity, gene expression, and cell-fate). |
| first_indexed | 2024-09-23T10:14:18Z |
| format | Article |
| id | mit-1721.1/142854 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:14:18Z |
| publishDate | 2022 |
| publisher | BioMed Central |
| record_format | dspace |
| spelling | mit-1721.1/1428542022-06-01T03:32:46Z Toward a more accurate 3D atlas of C. elegans neurons Skuhersky, Michael Wu, Tailin Yemini, Eviatar Nejatbakhsh, Amin Boyden, Edward Tegmark, Max Abstract Background Determining cell identity in volumetric images of tagged neuronal nuclei is an ongoing challenge in contemporary neuroscience. Frequently, cell identity is determined by aligning and matching tags to an “atlas” of labeled neuronal positions and other identifying characteristics. Previous analyses of such C. elegans datasets have been hampered by the limited accuracy of such atlases, especially for neurons present in the ventral nerve cord, and also by time-consuming manual elements of the alignment process. Results We present a novel automated alignment method for sparse and incomplete point clouds of the sort resulting from typical C. elegans fluorescence microscopy datasets. This method involves a tunable learning parameter and a kernel that enforces biologically realistic deformation. We also present a pipeline for creating alignment atlases from datasets of the recently developed NeuroPAL transgene. In combination, these advances allow us to label neurons in volumetric images with confidence much higher than previous methods. Conclusions We release, to the best of our knowledge, the most complete full-body C. elegans 3D positional neuron atlas, incorporating positional variability derived from at least 7 animals per neuron, for the purposes of cell-type identity prediction for myriad applications (e.g., imaging neuronal activity, gene expression, and cell-fate). 2022-05-31T19:50:55Z 2022-05-31T19:50:55Z 2022-05-28 2022-05-29T03:32:45Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/142854 BMC Bioinformatics. 2022 May 28;23(1):195 PUBLISHER_CC en https://doi.org/10.1186/s12859-022-04738-3 Creative Commons Attribution https://creativecommons.org/licenses/by/4.0 The Author(s) application/pdf BioMed Central BioMed Central |
| spellingShingle | Skuhersky, Michael Wu, Tailin Yemini, Eviatar Nejatbakhsh, Amin Boyden, Edward Tegmark, Max Toward a more accurate 3D atlas of C. elegans neurons |
| title | Toward a more accurate 3D atlas of C. elegans neurons |
| title_full | Toward a more accurate 3D atlas of C. elegans neurons |
| title_fullStr | Toward a more accurate 3D atlas of C. elegans neurons |
| title_full_unstemmed | Toward a more accurate 3D atlas of C. elegans neurons |
| title_short | Toward a more accurate 3D atlas of C. elegans neurons |
| title_sort | toward a more accurate 3d atlas of c elegans neurons |
| url | https://hdl.handle.net/1721.1/142854 |
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