Show simple item record

Single-cell characterization of antibody responses in coronavirus infection and COVID-19 vaccination

dc.contributor.advisorGeorgiev, Ivelin
dc.creatorKramer, Kevin John
dc.date.accessioned2021-10-13T13:19:00Z
dc.date.available2021-10-13T13:19:00Z
dc.date.created2021-09
dc.date.issued2021-09-08
dc.date.submittedSeptember 2021
dc.identifier.urihttp://hdl.handle.net/1803/16931
dc.description.abstractThe COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has resulted in the illness and death of millions of people worldwide. Although medical countermeasures including monoclonal antibody cocktails and vaccines have helped curb mortality rates, the emergence of more transmissible SARS-CoV-2 variants of concern highlights the need for continued genomic surveillance and therapeutic directions. This dissertation describes the B cell and antibody response against coronavirus infection and additionally the adaptive immune response mounted against COVID-19 vaccination. Single-cell analysis enables a nuanced characterization of immune cells in response to vaccination and infection which can often be overlooked when cellular populations are analyzed in bulk. LIBRA-seq, a sequencing-based antibody discovery technology, is an example of the advances in single-cell biology which can expedite antibody characterization campaigns while also resolving cross-reactivity patterns in a single read out. When applied to coronavirus infection samples, LIBRA-seq identified a panel of coronavirus cross-reactive antibodies as well as an additional set of potently neutralizing SARS-CoV-2 antibodies. In another application to longitudinal vaccine samples, LIBRA-seq mapped an IgM cross-reactive response with endemic coronaviruses at baseline which translated to plasmablast and memory SARS-like reactive IgA and IgG B cell populations post-vaccination. To complement analysis at the transcriptomic level, proteomic characterization utilizing mass cytometry revealed the enrichment of non-canonical memory T cell subsets as well as plasmablast B cell populations in donors post-vaccination. A machine-learning algorithm, T-REX, quantified the changes in immune subsets which were predicted to be antigen-specific and further confirmed by flow cytometric characterization. Given the severity of the COVID-19 pandemic to date and its unknown trajectory, the continued study of infection and vaccination may prove vital to address further developments of the SARS-CoV-2 in the future.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectCOVID-19
dc.subjectB cells
dc.subjectantibodies
dc.titleSingle-cell characterization of antibody responses in coronavirus infection and COVID-19 vaccination
dc.typeThesis
dc.date.updated2021-10-13T13:19:00Z
dc.type.materialtext
thesis.degree.namePhD
thesis.degree.levelDoctoral
thesis.degree.disciplineMolecular Pathology & Immunology
thesis.degree.grantorVanderbilt University Graduate School
dc.creator.orcid0000-0001-8451-9018
dc.contributor.committeeChairRathmell, Jeffrey


Files in this item

Icon

This item appears in the following Collection(s)

Show simple item record