Dataset Information

Quantitative T cell repertoire analysis by deep cDNA sequencing of T cell receptor ? and ? chains using next-generation sequencing (NGS).

ABSTRACT: Immune responses play a critical role in various disease conditions including cancer and autoimmune diseases. However, to date, there has not been a rapid, sensitive, comprehensive, and quantitative analysis method to examine T-cell or B-cell immune responses. Here, we report a new approach to characterize T cell receptor (TCR) repertoire by sequencing millions of cDNA of TCR ? and ? chains in combination with a newly-developed algorithm. Using samples from lung cancer patients treated with cancer peptide vaccines as a model, we demonstrate that detailed information of the V-(D)-J combination along with complementary determining region 3 (CDR3) sequences can be determined. We identified extensive abnormal splicing of TCR transcripts in lung cancer samples, indicating the dysfunctional splicing machinery in T lymphocytes by prior chemotherapy. In addition, we found three potentially novel TCR exons that have not been described previously in the reference genome. This newly developed TCR NGS platform can be applied to better understand immune responses in many disease areas including immune disorders, allergies, and organ transplantations.

SUBMITTER: Fang H

PROVIDER: S-EPMC4368121 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Quantitative T cell repertoire analysis by deep cDNA sequencing of T cell receptor α and β chains using next-generation sequencing (NGS).

Fang Hua H Yamaguchi Rui R Liu Xiao X Daigo Yataro Y Yew Poh Yin PY Tanikawa Chizu C Matsuda Koichi K Imoto Seiya S Miyano Satoru S Nakamura Yusuke Y

Oncoimmunology 20141201 12

Immune responses play a critical role in various disease conditions including cancer and autoimmune diseases. However, to date, there has not been a rapid, sensitive, comprehensive, and quantitative analysis method to examine T-cell or B-cell immune responses. Here, we report a new approach to characterize T cell receptor (TCR) repertoire by sequencing millions of cDNA of TCR α and β chains in combination with a newly-developed algorithm. Using samples from lung cancer patients treated with canc ...[more]

PMID: 25964866

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Project description:BackgroundIn recent years more than 20 assemblers have been proposed to tackle the hard task of assembling NGS data. A common heuristic when assembling a genome is to use several assemblers and then select the best assembly according to some criteria. However, recent results clearly show that some assemblers lead to better statistics than others on specific regions but are outperformed on other regions or on different evaluation measures. To limit these problems we developed GAM-NGS (Genomic Assemblies Merger for Next Generation Sequencing), whose primary goal is to merge two or more assemblies in order to enhance contiguity and correctness of both. GAM-NGS does not rely on global alignment: regions of the two assemblies representing the same genomic locus (called blocks) are identified through reads' alignments and stored in a weighted graph. The merging phase is carried out with the help of this weighted graph that allows an optimal resolution of local problematic regions.ResultsGAM-NGS has been tested on six different datasets and compared to other assembly reconciliation tools. The availability of a reference sequence for three of them allowed us to show how GAM-NGS is a tool able to output an improved reliable set of sequences. GAM-NGS is also a very efficient tool able to merge assemblies using substantially less computational resources than comparable tools. In order to achieve such goals, GAM-NGS avoids global alignment between contigs, making its strategy unique among other assembly reconciliation tools.ConclusionsThe difficulty to obtain correct and reliable assemblies using a single assembler is forcing the introduction of new algorithms able to enhance de novo assemblies. GAM-NGS is a tool able to merge two or more assemblies in order to improve contiguity and correctness. It can be used on all NGS-based assembly projects and it shows its full potential with multi-library Illumina-based projects. With more than 20 available assemblers it is hard to select the best tool. In this context we propose a tool that improves assemblies (and, as a by-product, perhaps even assemblers) by merging them and selecting the generating that is most likely to be correct.

Project description:BACKGROUND:Next generation sequencing (NGS) has become a universal practice in modern molecular biology. As the throughput of sequencing experiments increases, the preparation of conventional multiplexed libraries becomes more labor intensive. Conventional library preparation typically requires quality control (QC) testing for individual libraries such as amplification success evaluation and quantification, none of which occur until the end of the library preparation process. RESULTS:In this study, we address the need for a more streamlined high-throughput NGS workflow by tethering real-time quantitative PCR (qPCR) to conventional workflows to save time and implement single tube and single reagent QC. We modified two distinct library preparation workflows by replacing PCR and quantification with qPCR using SYBR Green I. qPCR enabled individual library quantification for pooling in a single tube without the need for additional reagents. Additionally, a melting curve analysis was implemented as an intermediate QC test to confirm successful amplification. Sequencing analysis showed comparable percent reads for each indexed library, demonstrating that pooling calculations based on qPCR allow for an even representation of sequencing reads. To aid the modified workflow, a software toolkit was developed and used to generate pooling instructions and analyze qPCR and melting curve data. CONCLUSIONS:We successfully applied fluorescent amplification for next generation sequencing (FA-NGS) library preparation to both plasmids and bacterial genomes. As a result of using qPCR for quantification and proceeding directly to library pooling, the modified library preparation workflow has fewer overall steps. Therefore, we speculate that the FA-NGS workflow has less risk of user error. The melting curve analysis provides the necessary QC test to identify and troubleshoot library failures prior to sequencing. While this study demonstrates the value of FA-NGS for plasmid or gDNA libraries, we speculate that its versatility could lead to successful application across other library types.

Dataset Information

Quantitative T cell repertoire analysis by deep cDNA sequencing of T cell receptor ? and ? chains using next-generation sequencing (NGS).

Publications

Quantitative T cell repertoire analysis by deep cDNA sequencing of T cell receptor α and β chains using next-generation sequencing (NGS).

Similar Datasets

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