Antigen identification and high-throughput interaction mapping by reprogramming viral entry

Dobson, Connor S.; Reich, Anna N.; Gaglione, Stephanie; Smith, Blake E.; Kim, Ellen J.; Dong, Jiayi; Ronsard, Larance; Okonkwo, Vintus; Lingwood, Daniel; Dougan, Michael; Dougan, Stephanie K.; Birnbaum, Michael E.

doi:10.1038/s41592-022-01436-z

Cited by 58 publications

(30 citation statements)

References 94 publications

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“…Although bulk and single-cell methods are limited to a modest number of antigen–MHC complexes per run, the advent of technologies such as lentiviral transfection assays 28 , 29 provides scalability to up to 96 antigen–MHC complexes through library-on-library screens . However, previous knowledge of the antigen–MHC complexes of interest is still required.…”

Section: State Of the Artmentioning

confidence: 99%

Can we predict T cell specificity with digital biology and machine learning?

et al. 2023

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Recent advances in machine learning and experimental biology have offered breakthrough solutions to problems such as protein structure prediction that were long thought to be intractable. However, despite the pivotal role of the T cell receptor (TCR) in orchestrating cellular immunity in health and disease, computational reconstruction of a reliable map from a TCR to its cognate antigens remains a holy grail of systems immunology. Current data sets are limited to a negligible fraction of the universe of possible TCR–ligand pairs, and performance of state-of-the-art predictive models wanes when applied beyond these known binders. In this Perspective article, we make the case for renewed and coordinated interdisciplinary effort to tackle the problem of predicting TCR–antigen specificity. We set out the general requirements of predictive models of antigen binding, highlight critical challenges and discuss how recent advances in digital biology such as single-cell technology and machine learning may provide possible solutions. Finally, we describe how predicting TCR specificity might contribute to our understanding of the broader puzzle of antigen immunogenicity.

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Section: State Of the Artmentioning

confidence: 99%

Can we predict T cell specificity with digital biology and machine learning?

et al. 2023

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“…There is no need for special organisms or genetic modifications, and the technique can easily be expanded to new antigens and T cell-APC pairs from both mice and humans. This strategy can, for instance, be integrated into validation efforts for antigen discovery and utilized in the investigation of the synapses of newly deorphanized human T cells, a largely unexplored area where tools are scarce [29][30][31][32]. Given that is a two-part system (tag and FlAsH), it also carries the added capability of being inducible at different time points to allow for increased flexibility in interrogating pMHCII-TCR interactions over time.…”

Section: Discussionmentioning

confidence: 99%

Illuminating T cell-dendritic cell interactions in vivo by FlAsHing antigens

Akkaya,

Souz,

Williams

et al. 2023

Preprint

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Delineating the complex network of interactions between antigen-specific T cells and antigen presenting cells (APCs) is crucial for effective precision therapies against cancer, chronic infections, and autoimmunity. However, the existing arsenal for examining antigen-specific T cell interactions is restricted to a select few antigen-T cell receptor pairs, with limited in situ utility. This lack of versatility is largely due to the disruptive effects of reagents on the immune synapse, which hinder real-time monitoring of antigen-specific interactions. To address this limitation, we have developed a novel and versatile immune monitoring strategy by adding a short cysteine-rich tag to antigenic peptides that emits fluorescence upon binding to thiol-reactive biarsenical hairpin compounds. Our findings demonstrate the specificity and durability of the novel antigen-targeting probes during dynamic immune monitoring in vitro and in vivo. This strategy opens new avenues for biological validation of T-cell receptors with newly identified epitopes by revealing the behavior of previously unrecognized antigen-receptor pairs, expanding our understanding of T cell responses.

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“…Birnbaum's team showed that yeast-display-trained models improve the prediction of peptide-binding affinity for pathogen-and tumor-associated peptides. To enable high-throughput screening of antigen-specific TCRs, they developed 'receptor-antigen pairing by targeted retroviruses' (RAPTR), which match TCRs with their cognate antigens based on specific infection of TCR-expressing cells by antigen-displaying viruses 7 . This technology enables the screening of single or polyclonal TCRs and further identification by bulk or single-cell sequencing.…”

Section: Meeting Reportmentioning

confidence: 99%