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Profiling tissue-resident T cell repertoires by RNA sequencing.

Brown SD, Raeburn LA, Holt RA - Genome Med (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, V5Z 1L3, Canada.

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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Related in: MedlinePlus

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
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Fig1: 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

Mentions: Conventional TCR-seq methods provide a detailed view of TCR diversity [2, 3, 12]. 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. 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 [13, 14]. 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. 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. 1). 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. This necessitates an analytical approach that is both fast and accurate for TCR extraction from tissue-derived RNA-seq datasets.Fig. 1


Profiling tissue-resident T cell repertoires by RNA sequencing.

Brown SD, Raeburn LA, Holt RA - Genome Med (2015)

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
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4666197&req=5

Fig1: 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
Mentions: Conventional TCR-seq methods provide a detailed view of TCR diversity [2, 3, 12]. 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. 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 [13, 14]. 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. 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. 1). 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. This necessitates an analytical approach that is both fast and accurate for TCR extraction from tissue-derived RNA-seq datasets.Fig. 1

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, V5Z 1L3, Canada.

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.

Show MeSH
Related in: MedlinePlus