bioRxivImmunologyPreprint
Evolution of T cell responses in the tuberculin skin test reveals generalisable Mtb-reactive T cell metaclones.
Carolin T. TurneriD, Andreas Tiffeau-MayeriD, Joshua RosenheimiD, Aneesh ChandraniD, Rishika Saxena, Ping ZhangiD, Jana Jiang, M. I. K. Berkeley, Flora Pang, Imran UddiniD, Gayathri NageswaraniD, Suzanne ByrneiD, Akshay Karthikeyan, Werner SmidtiD, Paul OgongoiD, Rachel Byng-MaddickiD, Santino CapocciiD, Marc C.I. LipmaniD, Heinke KunstiD, Stefan LozewicziD, Veron Rasmussen, Gabriele PollaraiD, Julian Charles KnightiD, Alasdair J. LeslieiD, Benny ChainiD, Mahdad NoursadeghiiD
Abstract
T cells contribute to immune protection and pathogenesis in tuberculosis, but measurements of polyclonal responses have failed to resolve correlates of outcome. We report the first temporal evaluation of the human in vivo clonal repertoire of Mtb-reactive T cell responses, by T cell receptor (TCR) sequencing at the site of a standardised antigenic challenge. Initial recruitment of non-Mtb reactive T cells is followed by enrichment of Mtb-reactive clones arising from oligoclonal T cell proliferation. We introduce a modular computational pipeline, Metaclonotypist, to sensitively cluster distinct TCRs with shared epitope specificity, which we apply here to establish a catalogue of public Mtb-reactive HLA-restricted T cell metaclones. Although most in vivo Mtb-reactive T cells are private, 10 metaclones were sufficient to identify Mtb-T cell reactivity across our study population (N[≥]128), indicating striking population level immunodominance of specific TCR-peptide interactions that may offer novel approaches to patient stratification and vaccine development.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
- Citations
- 2
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