Profiling tissue-resident T cell repertoires by RNA sequencing - PubMed (original) (raw)

Profiling tissue-resident T cell repertoires by RNA sequencing

Scott D Brown et al. Genome Med. 2015.

Abstract

Deep sequencing of recombined T cell receptor (TCR) genes and transcripts has provided a view of T cell repertoire diversity at an unprecedented resolution. Beyond profiling peripheral blood, analysis of tissue-resident T cells provides further insight into immune-related diseases. We describe the extraction of TCR sequence information directly from RNA-sequencing data from 6738 tumor and 604 control tissues, with a typical yield of 1 TCR per 10 million reads. This method circumvents the need for PCR amplification of the TCR template and provides TCR information in the context of global gene expression, allowing integrated analysis of extensive RNA-sequencing data resources.

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Figures

Fig. 1

Fig. 1

Schematic representation of T cell receptor sequencing (TCR-seq) versus RNA sequencing. Horizontal lines represent mRNA transcripts with gray poly-A tails. Each color represents a unique gene sequence. 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). 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. c RNA-seq employs shotgun sequencing, generating fragments from all transcripts present in the sample, which then have sequencing adapters ligated (black). The resulting sequencing library will contain fragments that, by chance, contain the CDR3 encoding sequence. Additionally, these libraries may contain fragments that share sequence similarity to recombined TCR sequences (e.g., the red transcript), potentially leading to false-positives

Fig. 2

Fig. 2

The number of reads containing CDR3 sequences varies across tumor sites. ACC adrenocortical carcinoma, BLCA bladder urothelial carcinoma, BRCA breast invasive carcinoma, CESC cervical squamous cell carcinoma and endocervical adenocarcinoma, CRAD colon and rectum adenocarcinoma, ESCA esophageal carcinoma, GBM glioblastoma multiforme, HNSC head and neck squamous cell carcinoma, KICH kidney chromophobe, KIRC kidney renal clear cell carcinoma, KIRP kidney renal papillary cell carcinoma, LGG brain lower grade glioma, LIHC liver hepatocellular carcinoma, LUAD lung adenocarcinoma, LUSC lung squamous cell carcinoma, OV ovarian serous cystadenocarcinoma, PAAD pancreatic adenocarcinoma, PCPG pheochromocytoma and paraganglioma, PRAD prostate adenocarcinoma, SKCM skin cutaneous melanoma, STAD stomach adenocarcinoma, TCR T cell receptor, THCA thyroid carcinoma, UCEC uterine corpus endometrial carcinoma, UCS uterine carcinosarcoma

Fig. 3

Fig. 3

The majority of CDR3s recovered from tumor/normal control tissue pairs are unique to tumor or normal tissue. For every CDR3, the number of reads in the set of tumors is plotted on the y-axis, with the number of reads in the set of normal samples on the x-axis. Circles are colored by the number of individuals in which that CDR3 sequence is detected

Fig. 4

Fig. 4

Sharing of CDR3β sequences. All 49,672 CDR3β sequences derived from tumors are plotted along the x-axis according to the number of tumors they are found in. Color defines the number of nucleotide sequences that were found to generate the same CDR3β amino acid sequence. The violin plot overlay shows that most recovered CDR3β sequences are unique to an individual, though there is notable sharing (4.4 %) between individuals. Known public viral-specific CDR3β sequences [31] are labeled with their antigen specificity. EBV Epstein–Barr virus

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