ArticleScience advances2026
Central T cell tolerance from sparse peptide sampling.
Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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8 authors.
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Abstract
Negative selection in the thymus limits autoimmunity by eliminating T cells that react strongly to self. Individual T cells, however, are only exposed to a small fraction of all self-peptides during their "training" in the thymus, and how tolerance is generalized to the remaining "test" self-peptides across peripheral tissues in the body remains an open question. We show that this can be achieved because the immune system satisfies two conditions necessary for generalization in machine learning settings. Consequently, sparse, random sampling of only 10% of self-peptides in the thymus is sufficient to avoid reactivity to 90% of peripheral self. We support this result and validate predictions from our model with diverse experimental data. Overall, we provide a plausible answer to a long-standing question underlying adaptive immunity, and we highlight how generalization, a fundamental challenge faced by nearly every learning algorithm, is tackled by the immune system.
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