3 research outputs found
Immune Fingerprinting through Repertoire Similarity
Immune repertoires provide a unique fingerprint reflecting the immune history
of individuals, with potential applications in precision medicine. However, the
question of how personal that information is and how it can be used to identify
individuals has not been explored. Here, we show that individuals can be
uniquely identified from repertoires of just a few thousands lymphocytes. We
present "Immprint," a classifier using an information-theoretic measure of
repertoire similarity to distinguish pairs of repertoire samples coming from
the same versus different individuals. Using published T-cell receptor
repertoires and statistical modeling, we tested its ability to identify
individuals with great accuracy, including identical twins, by computing false
positive and false negative rates from samples composed of 10,000
T-cells. We verified through longitudinal datasets and simulations that the
method is robust to acute infections and the passage of time. These results
emphasize the private and personal nature of repertoire data
NoisET: Noise learning and Expansion detection of T-cell receptors
High-throughput sequencing of T- and B-cell receptors makes it possible to
track immune repertoires across time, in different tissues, in acute and
chronic diseases and in healthy individuals. However quantitative comparison
between repertoires is confounded by variability in the read count of each
receptor clonotype due to sampling, library preparation, and expression noise.
We review methods for accounting for both biological and experimental noise and
present an easy-to-use python package NoisET that implements and generalizes a
previously developed Bayesian method. It can be used to learn experimental
noise models for repertoire sequencing from replicates, and to detect
responding clones following a stimulus. We test the package on different
repertoire sequencing technologies and datasets. We review how such approaches
have been used to identify responding clonotypes in vaccination and disease
data. Availability: NoisET is freely available to use with source code at
github.com/statbiophys/NoisET