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An immune response involves a coordinated orchestra of antigen-recognizing cells ( e.g. , T cells) and signaling molecules to mount a specific response against a pathogen. Although systems immunology offers a growing list of molecular interactions that are involved in antigen-specific immune responses, an understanding of how a response is mediated by different antigen characteristics is still lacking. On page 880 of this issue, Achar et al. ( 1 ) address this question by using a robotic platform to survey a broad range of functional T cell responses to different antigen stimulations. Using machine learning, they construct a simplified map that separates six different stereotypical classes of antigen-dependent immune responses. Understanding this antigen-encoding could help guide immunotherapy, including engineering chimeric antigen receptor (CAR)–T cells and identifying vaccine antigens.
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This content will become publicly available on February 21, 2026
Disease diagnostics using machine learning of B cell and T cell receptor sequences
Clinical diagnosis typically incorporates physical examination, patient history, various laboratory tests, and imaging studies but makes limited use of the human immune system’s own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis, an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to severe acute respiratory syndromecoronavirus2, influenza, and human immunodeficiency virus, highlight antigen-specific receptors, and reveal distinct characteristics of systemic lupus erythematosus and type-1 diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of immune responses.
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- Award ID(s):
- 2113389
- PAR ID:
- 10638758
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- Science
- Date Published:
- Journal Name:
- Science
- Volume:
- 387
- Issue:
- 6736
- ISSN:
- 0036-8075
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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