Decoding Human T Cell Immunity with Artificial Intelligence and Single-Cell Genomics

Area of Science:

• Immunology
• Genomics
• Bioinformatics

Background:

• T cells are crucial for adaptive immunity but their complexity hinders disease research.
• Understanding T cell receptor (TCR) diversity and function is vital for human disease insights.

Purpose of the Study:

• To review technological advancements in T cell research.
• To explore AI and single-cell genomics for T cell heterogeneity and antigen specificity.
• To address challenges and propose future strategies in T cell discovery.

Main Methods:

• Summarizing innovations in experimental T cell profiling.
• Highlighting machine learning for TCR-peptide-MHC (pMHC) interaction prediction.
• Discussing deep learning for structural modeling.

Main Results:

• Single-cell genomics and TCR sequencing offer new ways to study T cell heterogeneity.
• AI tools can predict TCR-pMHC interactions and map antigen specificity.
• Technological integration aids in understanding T cell roles in disease.

Conclusions:

• AI and single-cell genomics are key to dissecting T cell heterogeneity.
• Mapping TCR sequence to pMHC specificity is becoming feasible.
• Interpreting these features in clinical contexts will advance T cell research.