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Principles of Massively Parallel Sequencing for Engineering and Characterizing Gene Delivery

Citation

Brown, David (2022) Principles of Massively Parallel Sequencing for Engineering and Characterizing Gene Delivery. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/yqjm-6609. https://resolver.caltech.edu/CaltechTHESIS:02132022-064810187

Abstract

The advent of massively parallel sequencing and synthesis technologies have ushered in a new paradigm of biology, where high throughput screening of billions of nucleid acid molecules and production of libraries of millions of genetic mutants are now routine in labs and clinics. During my Ph.D., I worked to develop data analysis and experimental methods that take advantage of the scale of this data, while making the minimal assumptions necessary for deriving value from their application. My Ph.D. work began with the development of software and principles for analyzing deep mutational scanning data of libraries of engineered AAV capsids. By looking at not only the top variant in a round of directed evolution, but instead a broad distribution of the variants and their phenotypes, we were able to identify AAV variants with enhanced ability to transduce specific cells in the brain after intravenous injection. I then shifted to better understand the phenotypic profile of these engineered variants. To that end, I turned to single-cell RNA sequencing to seek to identify, with high resolution, the delivery profile of these variants in all cell types present in the cortex of a mouse brain. I began by developing infrastructure and tools for dealing with the data analysis demands of these experiments. Then, by delivering an engineered variant to the animal, I was able to use the single-cell RNA sequencing profile, coupled with a sequencing readout of the delivered genetic cargo present in each cell type, to define the variant’s tropism across the full spectrum of cell types in a single step. To increase the throughput of this experimental paradigm, I then worked to develop a multiplexing strategy for delivering up to 7 engineered variants in a single animal, and obtain the same high resolution readout for each variant in a single experiment. Finally, to take a step towards translation to human diagnostics, I leveraged the tools I built for scaling single-cell RNA sequencing studies and worked to develop a protocol for obtaining single-cell immune profiles of low volumes of self-collected blood. This study enabled repeat sampling in a short period of time, and revealed an incredible richness in individual variability and time-of-day dependence of human immune gene expression. Together, my Ph.D. work provides strategies for employing massively parallel sequencing and synthesis for new biological applications, and builds towards a future paradigm where personalized, high-resolution sequencing might be coupled with modular, customized gene therapy delivery.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:gene therapy; next-generation sequencing; transcriptomics; single-cell sequencing; protein engineering; machine learning
Degree Grantor:California Institute of Technology
Division:Biology and Biological Engineering
Major Option:Computation and Neural Systems
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Gradinaru, Viviana (advisor)
  • Thomson, Matthew (co-advisor)
Thesis Committee:
  • Yue, Yisong (chair)
  • Arnold, Frances Hamilton
  • Gradinaru, Viviana
  • Thomson, Matthew
Defense Date:31 August 2021
Non-Caltech Author Email:dibidave (AT) gmail.com
Funders:
Funding AgencyGrant Number
Shurl and Kay Curci FoundationUNSPECIFIED
Caltech CLARITY, Optogenetics and Vector Engineering Research (CLOVER)UNSPECIFIED
NIHDP1OD025535
Single-Cell Profiling and Engineering Center (SPEC at Caltech)UNSPECIFIED
Heritage Medical Research Institute (HMRI)UNSPECIFIED
Public Health Service (PHS)5T32NS105595-02
Record Number:CaltechTHESIS:02132022-064810187
Persistent URL:https://resolver.caltech.edu/CaltechTHESIS:02132022-064810187
DOI:10.7907/yqjm-6609
Related URLs:
URLURL TypeDescription
https://doi.org/10.1101/2021.06.25.449955DOIAdapted for Chapter 5
https://doi.org/10.1038/s41598-020-77073-3DOIAdapted for Chapter 6
https://doi.org/10.1038/s41592-020-0799-7DOIAdapted for Section 2.7
https://doi.org/10.3389/fimmu.2021.730825DOIFinal published version of Chapter 5
https://doi.org/10.22002/D1.1407DOICaltechDATA related to Chapter 6
https://doi.org/10.22002/D1.2090DOICaltechDATA related to Chapter 5
ORCID:
AuthorORCID
Brown, David0000-0002-9757-1744
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:14496
Collection:CaltechTHESIS
Deposited By: David Brown
Deposited On:12 Mar 2022 00:05
Last Modified:18 Mar 2022 23:51

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