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 data sets. 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.

Precise tracking of vaccine-responding T cell clones reveals convergent and personalized response in identical twins.

T cell receptor (TCR) repertoire data contain information about infections that could be used in disease diagnostics and vaccine development, but extracting that information remains a major challenge. Here we developed a statistical framework to detect TCR clone proliferation and contraction from longitudinal repertoire data. We applied this framework to data from three pairs of identical twins immunized with the yellow fever vaccine. We identified 600 to 1,700 responding TCRs in each donor and validated them using three independent assays. While the responding TCRs were mostly private, albeit with higher overlap between twins, they could be well-predicted using a classifier based on sequence similarity. Our method can also be applied to samples obtained postinfection, making it suitable for systematic discovery of new infection-specific TCRs in the clinic.

Invasions of Host-Associated Microbiome Networks.

The study of biological invasions of ecological systems has much to offer research on within-host systems, particularly for understanding infections and developing therapies using biological agents. Thanks to the ground-work established in other fields, such as community ecology and evolutionary biology, and to modern methods of measurement and quantification, the study of microbiomes has quickly become a field at the forefront of modern systems biology. Investigations of host-associated microbiomes (e.g., for studying human health) are often centered on measuring and explaining the structure, functions and stability of these communities. This momentum promises to rapidly advance our understanding of ecological networks and their stability, resilience and resistance to invasions. However, intrinsic properties of host-associated microbiomes that differ from those of free-living systems present challenges to the development of a within-host invasion ecology framework. The elucidation of principles underlying the invasibility of within-host networks will ultimately help in the development of medical applications and help shape our understanding of human health and disease.