Lentiviral Vector Engineering for High-Throughput Immune Profiling

The ability to decipher immune recognition is critical to understanding a broad range of diseases, including cancer, infection, and autoimmunity, as well as for the development of countermeasures such as vaccines and immunotherapy. Efforts to do so have been hampered by a lack of technologies that are capable of scaling to simultaneously capture the complexity of the adaptive immune repertoire and the landscape of potential antigens. Each individual’s immune repertoire consists of tens of millions of unique receptors that are responsible for surveying the trillions of possible antigens that might be encountered in one’s lifetime. As a result, there has been intense focus on the development of tools for screening large antigen sets or large collections of potential immune receptors, but most of these capture complexity on only one side of the interaction. We have therefore used synthetic virology approaches to engineer a “lentivirus surface display” platform capable of screening complex antigen mixtures against the full complexity of the adaptive immune repertoire. In Chapter 2 of this thesis, we describe our molecular engineering approaches that enabled the development of VSVGmut, an efficient and modular targeted pseudotyping strategy. In Chapter 3, we leverage VSVGmut and further advances to enable one-pot library on library antigen identification screens for T cells by displaying antigens on the surface of lentiviruses and encoding their identity in the viral genome. Antigen-specific viral infection of cells allows readout of both antigen and receptor identities via single-cell sequencing. In Chapters 4 and 5, we extend our approaches to B cells and present preliminary data for applications in both cellular and humoral profiling. Taken together, our approaches represent a new class of tools for identifying the molecular targets of the adaptive immune response at scale.

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

2022

Author dc:creator

• Dobson, Connor S.

Advisor dc:contributor.advisor

• Birnbaum, Michael E.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/162532

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/162532