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TCR transgenic clone selection guided by immune receptor analysis and single cell RNA expression of polyclonal responders (preprint)
biorxiv; 2024.
Preprint
in English
| bioRxiv | ID: ppzbmed-10.1101.2024.03.27.586931
ABSTRACT
Since the precursor frequency of naive T cells is extremely low, investigating the early steps of antigen-specific T cell activation is challenging. To overcome this detection problem, adoptive transfer of a cohort of T cells purified from T cell receptor (TCR) transgenic donors has been extensively used but is not readily available for emerging pathogens. Constructing TCR transgenic mice from T cell hybridomas is a labor-intensive and sometimes erratic process, since the best clones are selected based on antigen-induced CD69 upregulation or IL-2 production in vitro, and TCR chains are PCR-cloned into expression vectors. Here, we exploited the rapid advances in single cell sequencing and TCR repertoire analysis to select the best clones without hybridoma selection, and generated CORSET8 mice (CORona Spike Epitope specific CD8 T cell), carrying a TCR specific for the Spike protein of SARS-CoV-2. Implementing newly created DALI software for TCR repertoire analysis in single cell analysis enabled the rapid selection of the ideal responder CD8 T cell clone, based on antigen reactivity, proliferation and immunophenotype in vivo. In contrast, a traditional method based on hybridoma technology was unsuccessful. Identified TCR sequences were inserted as synthetic DNA into an expression vector and transgenic CORSET8 donor mice were created. After immunization with Spike/CpG-motifs, mRNA vaccination or SARS-CoV2 infection, CORSET8 T cells strongly proliferated and showed signs of T cell activation. Thus, a combination of TCR repertoire analysis and scRNA immunophenotyping allowed rapid selection of antigen-specific TCR sequences that can be used to generate TCR transgenic mice.
Full text:
Available
Collection:
Preprints
Database:
bioRxiv
Main subject:
Severe Acute Respiratory Syndrome
Language:
English
Year:
2024
Document Type:
Preprint
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