Tcr-seq

Tcr-seq, or T-cell receptor sequencing, refers to high-throughput sequencing methodologies used to identify and quantify the T-cell receptors (TCRs) present in a sample. These methods provide detailed information about the diversity, clonality, and abundance of T-cell populations.

TCRs are heterodimeric cell surface receptors on T cells that recognize antigens presented by major histocompatibility complex (MHC) molecules. Each TCR is composed of two chains, typically an alpha (α) and a beta (β) chain, though some T cells express gamma (γ) and delta (δ) chains. The variable (V), diversity (D), and joining (J) gene segments, along with junctional diversity introduced during recombination, generate the highly diverse repertoire of TCRs necessary for the adaptive immune system to recognize a wide range of antigens.

Tcr-seq technologies exploit the unique sequence of the complementarity-determining region 3 (CDR3) of the TCR chains, particularly the CDR3β, which is a key determinant of antigen specificity. By amplifying and sequencing the CDR3 region, researchers can identify and track individual T-cell clones within a sample.

Different Tcr-seq methods exist, including:

• Bulk Tcr-seq: This approach sequences TCRs from a population of cells, providing an aggregate view of the TCR repertoire. It allows for quantification of the frequency of specific TCR sequences within the entire sample.

• Single-cell Tcr-seq: This more advanced technique sequences the TCR from individual cells. This enables researchers to link TCR sequences with other cellular characteristics, such as gene expression profiles, allowing for a more comprehensive understanding of T-cell function and phenotype.

Tcr-seq is used in a variety of research and clinical applications, including:

• Immunoprofiling: Characterizing the TCR repertoire in healthy individuals and patients with various diseases, such as cancer, autoimmune disorders, and infectious diseases.

• Monitoring immune responses: Tracking changes in the TCR repertoire in response to vaccination, immunotherapy, or infection.

• Identifying tumor-infiltrating lymphocytes (TILs): Analyzing the TCR repertoire of TILs to understand the anti-tumor immune response.

• Developing personalized immunotherapies: Identifying TCR sequences that recognize tumor-specific antigens and using this information to develop targeted immunotherapies.

The data generated from Tcr-seq experiments require specialized bioinformatics tools and pipelines for analysis, including sequence alignment, error correction, clonotype identification, and diversity estimation.