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 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.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Biological Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Le, Hong Anh Anna

Advisor dc:contributor.advisor

• Blainey, Paul C.

Statement dc:rights

• Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
• Copyright retained by author(s)

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-sa/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/153025

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/153025