Functional genomic and transcriptomic tools for spatial and dynamic phenotypes
Biology is driven by complex cellular processes that require precise regulation in time and in space. However, the genetic and molecular factors underlying these behaviors are difficult to study in their native contexts and, as a result, are often not well understood. Although next-generation sequen...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2023
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| Online Access: | https://hdl.handle.net/1721.1/153025 https://orcid.org/0009-0000-4207-8998 |
| _version_ | 1811091819907252224 |
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| author | Le, Hong Anh Anna |
| author2 | Blainey, Paul C. |
| author_facet | Blainey, Paul C. Le, Hong Anh Anna |
| author_sort | Le, Hong Anh Anna |
| collection | MIT |
| description | Biology is driven by complex cellular processes that require precise regulation in time and in space. However, the genetic and molecular factors underlying these behaviors are difficult to study in their native contexts and, as a result, are often not well understood. Although next-generation sequencing and image-based methods have enabled high-throughput profiling of cell states, there is still a need for technologies that systematically probe and measure complex behaviors, including cell non-autonomous and dynamic phenotypes.
In this thesis, we present the development of functional genomic and synthetic biology tools to address this challenge. We first applied optical pooled screening to quantify cell-cell interactions in mixed cultures with primary neurons and reveal functional interaction partners of synaptogenic cell adhesion molecules. Using these screens, we identified differential modulators of excitatory and inhibitory synapse formation, implicating diverse cellular pathways in this process. To increase the throughput of these optical pooled screens, we also built a fluidics platform for automated in situ sequencing. Finally, we leveraged retroviral polyproteins to package cellular RNAs for non-destructive measurements, enabling longitudinal recording of transcriptional states in living cells. Together, this work establishes scalable tools to measure and understand spatial and dynamic cellular phenotypes. |
| first_indexed | 2024-09-23T15:08:33Z |
| format | Thesis |
| id | mit-1721.1/153025 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T15:08:33Z |
| publishDate | 2023 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1530252023-11-28T03:10:34Z Functional genomic and transcriptomic tools for spatial and dynamic phenotypes Le, Hong Anh Anna Blainey, Paul C. Massachusetts Institute of Technology. Department of Biological Engineering Biology is driven by complex cellular processes that require precise regulation in time and in space. However, the genetic and molecular factors underlying these behaviors are difficult to study in their native contexts and, as a result, are often not well understood. Although next-generation sequencing and image-based methods have enabled high-throughput profiling of cell states, there is still a need for technologies that systematically probe and measure complex behaviors, including cell non-autonomous and dynamic phenotypes. In this thesis, we present the development of functional genomic and synthetic biology tools to address this challenge. We first applied optical pooled screening to quantify cell-cell interactions in mixed cultures with primary neurons and reveal functional interaction partners of synaptogenic cell adhesion molecules. Using these screens, we identified differential modulators of excitatory and inhibitory synapse formation, implicating diverse cellular pathways in this process. To increase the throughput of these optical pooled screens, we also built a fluidics platform for automated in situ sequencing. Finally, we leveraged retroviral polyproteins to package cellular RNAs for non-destructive measurements, enabling longitudinal recording of transcriptional states in living cells. Together, this work establishes scalable tools to measure and understand spatial and dynamic cellular phenotypes. Ph.D. 2023-11-27T15:21:48Z 2023-11-27T15:21:48Z 2023-09 2023-11-16T01:06:21.852Z Thesis https://hdl.handle.net/1721.1/153025 https://orcid.org/0009-0000-4207-8998 Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) Copyright retained by author(s) https://creativecommons.org/licenses/by-sa/4.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Le, Hong Anh Anna Functional genomic and transcriptomic tools for spatial and dynamic phenotypes |
| title | Functional genomic and transcriptomic tools for spatial and dynamic phenotypes |
| title_full | Functional genomic and transcriptomic tools for spatial and dynamic phenotypes |
| title_fullStr | Functional genomic and transcriptomic tools for spatial and dynamic phenotypes |
| title_full_unstemmed | Functional genomic and transcriptomic tools for spatial and dynamic phenotypes |
| title_short | Functional genomic and transcriptomic tools for spatial and dynamic phenotypes |
| title_sort | functional genomic and transcriptomic tools for spatial and dynamic phenotypes |
| url | https://hdl.handle.net/1721.1/153025 https://orcid.org/0009-0000-4207-8998 |
| work_keys_str_mv | AT lehonganhanna functionalgenomicandtranscriptomictoolsforspatialanddynamicphenotypes |