Detecting T cell receptors involved in immune responses from single repertoire snapshots.
Mikhail V PogorelyyAnastasia A MinervinaMikhail ShugayDmitriy M ChudakovYury B LebedevThierry MoraAleksandra M WalczakPublished in: PLoS biology (2019)
Hypervariable T cell receptors (TCRs) play a key role in adaptive immunity, recognizing a vast diversity of pathogen-derived antigens. Our ability to extract clinically relevant information from large high-throughput sequencing of TCR repertoires (RepSeq) data is limited, because little is known about TCR-disease associations. We present Antigen-specific Lymphocyte Identification by Clustering of Expanded sequences (ALICE), a statistical approach that identifies TCR sequences actively involved in current immune responses from a single RepSeq sample and apply it to repertoires of patients with a variety of disorders - patients with autoimmune disease (ankylosing spondylitis [AS]), under cancer immunotherapy, or subject to an acute infection (live yellow fever [YF] vaccine). We validate the method with independent assays. ALICE requires no longitudinal data collection nor large cohorts, and it is directly applicable to most RepSeq datasets. Its results facilitate the identification of TCR variants associated with diseases and conditions, which can be used for diagnostics and rational vaccine design.
Keyphrases
- regulatory t cells
- immune response
- ankylosing spondylitis
- high throughput sequencing
- dendritic cells
- electronic health record
- big data
- rna seq
- disease activity
- liver failure
- rheumatoid arthritis
- drug induced
- toll like receptor
- oxidative stress
- single cell
- bioinformatics analysis
- high throughput
- genome wide
- copy number
- dna methylation
- respiratory failure
- systemic lupus erythematosus
- cross sectional
- data analysis
- genetic diversity
- finite element
- anti inflammatory