iCAT

iCAT analyzes high-throughput T-cell receptor (TCR) sequencing data to identify TCR sequences statistically associated with pathogen exposure and to classify samples by pathogen-specific immune signatures.

Key Features:

• Pathogen-Specific TCR Identification: Analyzes exposed (positive) and unexposed (negative) samples to identify TCR sequences statistically associated with pathogen exposure.
• Classification of T-Cell Receptor Clonotypes: Classifies independent samples based on identified pathogen-associated TCR clonotypes to diagnose pathogen-specific exposures.
• Classifier Training: Uses statistical associations between TCR sequences and exposure status to train a predictive classifier for independent sample classification.
• Predictive Performance: Demonstrated high accuracy across datasets: 95% prediction accuracy for vaccine-associated TCRs in mice; 95% accuracy distinguishing smallpox-vaccinated versus naïve mice; 100% accuracy distinguishing naïve and monkeypox-vaccinated mice; and perfect classification of human SARS-CoV-2–exposed samples.

Scientific Applications:

• Infection Diagnostics: Enables diagnosis of pathogen exposure by identifying pathogen-specific TCR signatures from sequencing data.
• Immunological Response Research: Supports investigation of T-cell–mediated immune responses and tracking of immunological exposure history.
• Vaccine Response Assessment: Assists evaluation of vaccine-associated TCR responses in animal and human studies.
• Pathogen-Specific Studies: Applicable to analysis of TCR responses to pathogens including smallpox, monkeypox, and SARS-CoV-2.

Methodology:

Analyzes TCR sequencing data from exposed and unexposed samples to statistically associate specific TCR sequences with pathogen exposure and trains a classifier on those associations to classify independent samples.