Therefore, we optimized settings using positive and negative control RNA-seq data. Closer inspection of the bogus CDR3s identified low similarity between these sequences and the putative flanking TCR V and J gene segments, suggesting that the false positives were spurious, non-TCR hits to TCR-like sequences elsewhere in the transcriptome. However, upon initial application of MiTCR to tumor RNA-seq data using the default parameters, we identified hundreds of non-specific and out-of-frame CDR3 sequences per sample, which prompted us to explore alternative parameters. We deployed MiTCR v1.0.3, which is well suited for the annotation of CDR3 sequences from sequencing reads. Extraction of T cell receptor CDR3 sequences from RNA-seq data All clinical specimens not part of The Cancer Genome Atlas (TCGA) were obtained previously with informed consent by the British Columbia Cancer Agency Tumor Tissue Repository, which operates as a dedicated biobank with approval from the University of British Columbia-British Columbia Cancer Agency Research Ethics Board (certificate #H09-01268). The research described herein conformed to the Helsinki Declaration. Additionally, these libraries may contain fragments that share sequence similarity to recombined TCR sequences (e.g., the red transcript), potentially leading to false-positives The resulting sequencing library will contain fragments that, by chance, contain the CDR3 encoding sequence. c RNA-seq employs shotgun sequencing, generating fragments from all transcripts present in the sample, which then have sequencing adapters ligated ( black). b TCR-seq involves selective amplification of the CDR3 region of TCR transcripts (displayed as a color gradient) by reverse transcription polymerase chain reaction (PCR) shown using a conserved C-gene primer ( purple with black sequencing adapter tails) for the initial reverse transcription step and resulting, after PCR (not shown), in an enriched set of recombined TCR sequences. a A pool of all mRNA in a sample is depicted, which contains irrelevant transcripts ( blue, brown, and red) as well as recombined TCR transcripts ( multi-colored). Each color represents a unique gene sequence. Horizontal lines represent mRNA transcripts with gray poly-A tails. Schematic representation of T cell receptor sequencing ( TCR-seq) versus RNA sequencing. This necessitates an analytical approach that is both fast and accurate for TCR extraction from tissue-derived RNA-seq datasets. For a pure lymphocyte population, only one in approximately 2000 transcripts are TCR transcripts (see “ Methods”) and in tissues T cells represent a minor cell type, further decreasing TCR transcript representation. Compared to TCR-seq, the main challenge in CDR3 extraction from tumor RNA-seq data is the disproportionally large number of non-TCR transcripts (Fig. Here, we describe an optimized approach for TCR CDR3 extraction from RNA-seq datasets from solid tumors, for the purpose of characterizing T cell populations present in the tumor environment.
#LOSSIUS A IMMUNE REPERTOIRE SOFTWARE#
Next-generation sequencing technology has made whole-genome and transcriptome sequencing routine, and provided opportunities for the extraction of immunological data, such as human leukocyte antigen (HLA) type, using specialized software tools. However, because they rely on targeted amplicon sequencing, they do not evaluate TCR variation in the context of the overall genetic diversity of the specimen from which the data are derived. Ĭonventional TCR-seq methods provide a detailed view of TCR diversity. TCR-seq applied to tissue specimens can provide insight into tumor-infiltrating lymphocytes, T cells associated with autoimmune pathology and infection, and the properties of normal primary and secondary lymphatic tissues. Typically, CDR3 sequence information is acquired by performing TCR-sequencing (TCR-seq) experiments on peripheral T cells isolated from blood amplifying the CDR3 region with a conserved C gene primer followed by 5′ rapid amplification of cDNA ends, or with a multiplexed set of V and J gene primers. CDR3 is the TCR motif that directly binds MHC-presented peptide epitopes and this binding interaction is the main factor conferring T cell antigen specificity. T cells recognize peptide epitopes presented on the surface of cells on major histocompatibility complex (MHC) molecules. Primary sequence analysis of the highly variable complementarity determining region 3 (CDR3) of rearranged T cell receptor (TCR) genes provides insight into the adaptive immune response.
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