Abstract

Simple SummaryThe recent advancement in high-throughput sequencing has become indispensable for immune-genomics and profiling the T- and B-cell receptor repertoires. Immune repertoire sequencing (IR-seq) and whole transcriptome sequencing (WTS) can be implemented to investigate and quantitatively characterize the complex pattern of the CDR3 region. We conducted T-cell diversity analysis result comparisons of these sequencing methods and suggest an intuitive approach to discriminate reliable TCR sequences and clonotype patterns from capturing errors. Although bulk-RNA sequencing is commonly used for cancer analysis, we confirmed capturing highly enriched TCR transcripts with IR-seq is more reliable for accurate immune repertoire discovery, and singleton read filtering criteria should be applied to capture true clonotypes from error-prone sequencing data. The use of such well-established data and analytical methodologies can broaden understanding of antigen specificity in immunity and enabling efficient therapeutic antibody finding.Analysis of the T-cell receptor (TCR) repertoire is essential to characterize the extensive collections of T-cell populations with recognizing antigens in cancer research, and whole transcriptome sequencing (WTS) and immune repertoire sequencing (IR-seq) are commonly used for this measure. To date, no standard read filtering method for IR measurement has been presented. We assessed the diversity of the TCR repertoire results from the paired WTS and IR-seq data of 31 multiple myeloma (MM) patients. To invent an adequate read filtering strategy for IR analysis, we conducted comparisons with WTS results. First, our analyses for determining an optimal threshold for selecting clonotypes showed that the clonotypes supported by a single read largely affected the shared clonotypes and manifested distinct patterns of mapping qualities, unlike clonotypes with multiple reads. Second, although IR-seq could reflect a wider TCR region with a higher capture rate than WTS, an adequate comparison with the removal of unwanted bias from potential sequencing errors was possible only after applying our read filtering strategy. As a result, we suggest that TCR repertoire analysis be carried out through IR-seq to produce reliable and accurate results, along with the removal of single-read clonotypes, to conduct immune research in cancer using high-throughput sequencing.

Highlights

  • The most important function of the immune system is to recognize antigens, defend by increasing the number of immune cells that make antibodies and kill external pathogens such as bacteria and viruses [1]

  • Diversity and flexibility are essential for these T-cell receptor (TCR) to respond to extremely diverse antigens, and these are determined by random rearrangements of VDJ genes during the development of T-cells to produce a variety of complementarity-determining region (CDR) sequences for each clone [7]

  • RNA-seq using STAR alignment, TCR region mapped reads and number of unique TCR clonotypes) shown in Table 2 suggest that the coverage depth of each CDR3 was captured at high rates with immune repertoire sequencing (IR-seq), which agrees with our expectation, and more diversified amino acid (AA) clonotypes were detected than those detected with RNA-seq

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Summary

Introduction

The most important function of the immune system is to recognize antigens, defend by increasing the number of immune cells that make antibodies and kill external pathogens such as bacteria and viruses [1]. RNA-seq-based TCR profiling is limited in detecting a wide range of TCR regions since it infers the results by extracting only CDR3 regions belonging to a portion of the entire transcript [20] Due to their low abundance, capturing the mostly highly enriched TCR transcripts could be a more reliable but incomplete estimation of TCR repertoires from other random reads [13,20]. We present an intuitive approach for reducing putative errors associated with IR-seq to increase the accuracy of the estimated immune repertoire This led us to assess T-cell clonality and the results of both sequencing methods and quality controls

Study Population and Basic Characteristics of TCR Sequences
T-cell repertoire distributions
TCR Repertoire Diversity in MM Samples from IR-seq and RNA-seq
Proper Read Filtering and Clonotype Abundances at the Single-Patient Level
Assessment of TCR Genes from IR-seq and RNA-seq after Read Filtering
Repertoire Inference Using Random Sampling
TCRinclonality pattern randomly
Discussion
RNA Sequencing
Immunoverse Sequencing
Data Analysis and TCR Repertoire Extraction
Conclusions
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