Universal antigen encoding of T cell activation from high-dimensional cytokine dynamics

Achar, Sooraj; Bourassa, François X. P.; Rademaker, Thomas J.; Lee, Angela; Kondo, Taisuke; Salazar-Cavazos, Emanuel; Davies, John Stephen; Taylor, Naomi; François, Paul; Altan‐Bonnet, Grégoire

doi:10.1126/science.abl5311

Cited by 38 publications

(32 citation statements)

References 45 publications

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“…Many predictors are trained using epitopes from the Immune Epitope Database labelled with readouts from single time points 7 . However, Achar et al 75 illustrated that integrating cytokine responses over time improved prediction of quality. Antigen load and affinity can also play important roles 74 , 76 .…”

Section: Key Challengesmentioning

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: Key Challengesmentioning

confidence: 99%

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

et al. 2023

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“…Mounting experimental evidence indicates that the TCR (as a case study for KPR) may indeed produce multiple intracellular second messengers which may enable T cells to detect antigen quantity alongside antigen quality (78)(79)(80)(81)(82). Our results lay the ground for further theoretical work aimed at the understanding the effects of noise in multi-output receptor signaling pathways (78,(83)(84)(85).…”

Section: Bmentioning

confidence: 71%

“…These outputs can be used to infer the presence of multiple ligands in a mixture (53), convey information about ligand concentration or other environmental conditions in addition to ligand identity (77), or perform other complex signaling processing tasks (78). Mounting experimental evidence indicates that the TCR (as a case study for KPR) may indeed produce multiple intracellular second messengers which may enable T cells to detect antigen quantity alongside antigen quality (78)(79)(80)(81)(82). Our results lay the ground for further theoretical work aimed at the understanding the effects of noise in multi-output receptor signaling pathways (78,(83)(84)(85).…”

Section: Bmentioning

confidence: 99%

Proofreading Is Too Noisy For Effective Ligand Discrimination

Kirby

Zilman

2023

Preprint

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Kinetic proofreading (KPR) has been used as a paradigmatic explanation for the high specificity of important biological processes including ligand discrimination by cellular receptors. Kinetic proofreading enhances the difference in the mean receptor occupancy between different ligands, thus potentially enabling better discrimination. On the other hand, proofreading also attenuates the signal, increasing the relative magnitude of noise in the downstream signal. This can interfere with reliable ligand discrimination. To understand the effect of noise on ligand discrimination beyond the comparison of the mean signals, we formulate the task of ligand discrimination as a problem of statistical estimation of the molecular affinity of ligands. Our analysis reveals that proofreading typically worsens ligand resolution which decreases with the number of proofreading steps under most commonly considered conditions. This contrasts with the usual notion that kinetic proofreading universally improves ligand discrimination with additional proofreading steps. Our results are consistent across a variety of different proofreading schemes, suggesting that they are inherent to the KPR mechanism itself rather than any particular model of molecular noise. Based on our results, we suggest alternative roles for kinetic proofreading schemes such as multiplexing and combinatorial encoding in multi-ligand/multi-output pathways.

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“…tolerance, TH-1, TH-2, or TH-17 responses…). In this context, Achar et al have recently reported that distinct cellular responses of the T cells are elicited by antigenic peptides of different affinity for the OT-I TCR (Achar et al, 2022). Peptide affinity influences both the half-life of TCR-pMHC binding, and the stability of T cell/APC interactions (Moreau et al, 2012).…”

Section: Resultsmentioning

confidence: 99%

Light-inducible T cell engagers trigger, tune and shape the activation of primary T cells

Jaeger

Anastasio

Brustlein

et al. 2022

Preprint

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Cells perceive overtime complex sequences of receptor stimulation that they integrate to mount an appropriate response. Yet, the influence of signal dynamics on cell responses has been poorly characterized due to technical limitations. Here, we present a generalizable approach to control receptor stimulation on unmodified primary cells. Indeed, for applications on primary murine T cells, we have engineered the LiTe system, a new recombinant optogenetics-based Light-inducible T cell engager which allows tunable and reversible spatiotemporal control of the T Cell Receptor (TCR) stimulation. We also provided in vitro evidence that this system enables efficient T cell activation with light, leading to cytokine secretion or tumor cell killing. Using specific time-gated stimulations, we have been able to orient the outcome of the activation of T cells. Overall, the LiTe system constitutes a versatile ON/OFF molecular switch allowing to decipher the cellular response to stimulation dynamics. Its original control over T cell activation opens new avenues for future precision cancer immunotherapy.

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Universal antigen encoding of T cell activation from high-dimensional cytokine dynamics

Cited by 38 publications

References 45 publications

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

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

Proofreading Is Too Noisy For Effective Ligand Discrimination

Light-inducible T cell engagers trigger, tune and shape the activation of primary T cells

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