Fast image and data processing methods for novel neuroscience technologies
The nematode C. elegans, a transparent animal with 302 neurons, is a suitable model organism for whole-brain measurement of nervous activity. However, under panneuronal labeling, it is difficult to resolve the identity of the neurons by shape or location alone. We propose a fluorescent in situ hybri...
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| Other Authors: | |
| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/143327 |
| _version_ | 1826199411200360448 |
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| author | Çeliker, Orhan Tunç |
| author2 | Boyden, Edward S. |
| author_facet | Boyden, Edward S. Çeliker, Orhan Tunç |
| author_sort | Çeliker, Orhan Tunç |
| collection | MIT |
| description | The nematode C. elegans, a transparent animal with 302 neurons, is a suitable model organism for whole-brain measurement of nervous activity. However, under panneuronal labeling, it is difficult to resolve the identity of the neurons by shape or location alone. We propose a fluorescent in situ hybridization (FISH) based pipeline for reading out gene expression from neurons. Using optimization methods, we select a compact set of genes that provide enough information to distinguish every neighboring pair of neurons in the nervous system. We show that we can process volumetric images of live and fixed C. elegans to read out the gene expression patterns of each observed neuron and match it to their calcium indicator data. Separately, we also outline computational approaches to processing fluorescence data from novel fluorescent sensors. |
| first_indexed | 2024-09-23T11:19:46Z |
| format | Thesis |
| id | mit-1721.1/143327 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:19:46Z |
| publishDate | 2022 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1433272022-06-16T03:36:10Z Fast image and data processing methods for novel neuroscience technologies Çeliker, Orhan Tunç Boyden, Edward S. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science The nematode C. elegans, a transparent animal with 302 neurons, is a suitable model organism for whole-brain measurement of nervous activity. However, under panneuronal labeling, it is difficult to resolve the identity of the neurons by shape or location alone. We propose a fluorescent in situ hybridization (FISH) based pipeline for reading out gene expression from neurons. Using optimization methods, we select a compact set of genes that provide enough information to distinguish every neighboring pair of neurons in the nervous system. We show that we can process volumetric images of live and fixed C. elegans to read out the gene expression patterns of each observed neuron and match it to their calcium indicator data. Separately, we also outline computational approaches to processing fluorescence data from novel fluorescent sensors. Ph.D. 2022-06-15T13:12:49Z 2022-06-15T13:12:49Z 2022-02 2022-03-04T20:47:40.841Z Thesis https://hdl.handle.net/1721.1/143327 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Çeliker, Orhan Tunç Fast image and data processing methods for novel neuroscience technologies |
| title | Fast image and data processing methods for novel neuroscience technologies |
| title_full | Fast image and data processing methods for novel neuroscience technologies |
| title_fullStr | Fast image and data processing methods for novel neuroscience technologies |
| title_full_unstemmed | Fast image and data processing methods for novel neuroscience technologies |
| title_short | Fast image and data processing methods for novel neuroscience technologies |
| title_sort | fast image and data processing methods for novel neuroscience technologies |
| url | https://hdl.handle.net/1721.1/143327 |
| work_keys_str_mv | AT celikerorhantunc fastimageanddataprocessingmethodsfornovelneurosciencetechnologies |