Ultrasensitive DNA Immune Repertoire Sequencing Using Unique Molecular Identifiers

γδ T cells in human colon adenocarcinomas comprise mainly Vδ1, Vδ2, and Vδ3 cells with distinct phenotype and function

Γδ T cell infiltration into tumours usually correlates with improved patient outcome, but both tumour-promoting and tumoricidal effects of γδ T cells have been documented. Human γδ T cells can be divided into functionally distinct subsets based on T cell receptor (TCR) Vδ usage. Still, the contribution of these different subsets to tumour immunity remains elusive. Here, we provide a detailed γδ T cell profiling in colon tumours, using mass and flow cytometry, mRNA quantification, and TCR sequencing. δ chain usage in both the macroscopically unaffected colon mucosa and tumours varied considerably between patients, with substantial fractions of Vδ1, Vδ2, and non-Vδ1 Vδ2 cells. Sequencing of the Vδ complementarity-determining region 3 showed that almost all non-Vδ1 Vδ2 cells used Vδ3 and that tumour-infiltrating γδ clonotypes were unique for every patient. Non-Vδ1Vδ2 cells from colon tumours expressed several activation markers but few NK cell receptors and exhaustion markers. In addition, mRNA analyses showed that non-Vδ1 Vδ2 cells expressed several genes for proteins with tumour-promoting functions, such as neutrophil-recruiting chemokines, Galectin 3, and transforming growth factor-beta induced. In summary, our results show a large variation in γδ T cell subsets between individual tumours, and that Vδ3 cells make up a substantial proportion of γδ T cells in colon tumours. We suggest that individual γδ T cell composition in colon tumours may contribute to the balance between favourable and adverse immune responses, and thereby also patient outcome.

The performance of homopolymer detection using dichromatic and tetrachromatic fluorogenic next-generation sequencing platforms

OBJECTIVES

Homopolymer (HP) sequencing is error-prone in next-generation sequencing (NGS) assays, and may induce false insertion/deletions and substitutions. This study aimed to evaluate the performance of dichromatic and tetrachromatic fluorogenic NGS platforms when sequencing homopolymeric regions.

RESULTS

A HP-containing plasmid was constructed and diluted to serial frequencies (3%, 10%, 30%, 60%) to determine the performance of an MGISEQ-2000, MGISEQ-200, and NextSeq 2000 in HP sequencing. An evident negative correlation was observed between the detected frequencies of four nucleotide HPs and the HP length. Significantly decreased rates (P < 0.01) were found in all 8-mer HPs in all three NGS systems at all four expected frequencies, except in the NextSeq 2000 at 3%. With the application of a unique molecular identifier (UMI) pipeline, there were no differences between the detected frequencies of any HPs and the expected frequencies, except for poly-G 8-mers using the MGI 200 platform. UMIs improved the performance of all three NGS platforms in HP sequencing.

CONCLUSIONS

We first constructed an HP-containing plasmid based on an EGFR gene backbone to evaluate the performance of NGS platforms when sequencing homopolymeric regions. A highly comparable performance was observed between the MGISEQ-2000 and NextSeq 2000, and introducing UMIs is a promising approach to improve the performance of NGS platforms in sequencing homopolymeric regions.

T-cell receptor diversity and allergen sensitivity in childhood asthma and atopic dermatitis

BACKGROUND

Childhood allergies of asthma and atopic dermatitis (AD) involve an overactive T-cell immune response triggered by allergens. However, the impact of T-cell receptor (TCR) repertoires on allergen sensitization and their role in mediating different phenotypes of asthma and AD in early childhood remains unclear.

METHODS

A total of 78 children, comprising 26 with asthma alone, 26 with AD alone, and 26 healthy controls (HC), were enrolled. TCR repertoire profiles were determined using a unique molecular identifier system for next-generation sequencing. Integrative analyses of their associations with allergen-specific IgE levels and allergies were performed.

RESULTS

The diversity in TCR alpha variable region (TRAV) genes of TCR repertoires and complementarity determining region 3 (CDR3) clonality in TRAV/TRBV (beta) genes were significantly higher in children with AD compared with those with asthma and HC (p < .05). Compared with HC, the expression of TRAV13-1 and TRAV4 genes was significantly higher in both asthma and AD (p < .05), with a significant positive correlation with mite-specific IgE levels (p < .01). In contrast, TRBV7-9 gene expression was significantly lower in both asthma and AD (p < .01), with this gene showing a significant negative correlation with mite-specific IgE levels (p < .01). Furthermore, significantly higher TRAV8-3 gene expression, positively correlated with food-specific IgE levels, was found in children with AD compared with those with asthma (p < .05).

CONCLUSION

Integrated TCR repertoires analysis provides clinical insights into the diverse TCR genes linked to antigen specificity, offering potential for precision immunotherapy in childhood allergies.

Principles of digital sequencing using unique molecular identifiers

Massively parallel sequencing technologies have long been used in both basic research and clinical routine. The recent introduction of digital sequencing has made previously challenging applications possible by significantly improving sensitivity and specificity to now allow detection of rare sequence variants, even at single molecule level. Digital sequencing utilizes unique molecular identifiers (UMIs) to minimize sequencing-induced errors and quantification biases. Here, we discuss the principles of UMIs and how they are used in digital sequencing. We outline the properties of different UMI types and the consequences of various UMI approaches in relation to experimental protocols and bioinformatics. Finally, we describe how digital sequencing can be applied in specific research fields, focusing on cancer management where it can be used in screening of asymptomatic individuals, diagnosis, treatment prediction, prognostication, monitoring treatment efficacy and early detection of treatment resistance as well as relapse.

Digital RNA sequencing using unique molecular identifiers enables ultrasensitive RNA mutation analysis

Mutation analysis is typically performed at the DNA level since most technical approaches are developed for DNA analysis. However, some applications, like transcriptional mutagenesis, RNA editing and gene expression analysis, require RNA analysis. Here, we combine reverse transcription and digital DNA sequencing to enable low error digital RNA sequencing. We evaluate yield, reproducibility, dynamic range and error correction rate for seven different reverse transcription conditions using multiplexed assays. The yield, reproducibility and error rate vary substantially between the specific conditions, where the yield differs 9.9-fold between the best and worst performing condition. Next, we show that error rates similar to DNA sequencing can be achieved for RNA using appropriate reverse transcription conditions, enabling detection of mutant allele frequencies <0.1% at RNA level. We also detect mutations at both DNA and RNA levels in tumor tissue using a breast cancer panel. Finally, we demonstrate that digital RNA sequencing can be applied to liquid biopsies, analyzing cell-free gene transcripts. In conclusion, we demonstrate that digital RNA sequencing is suitable for ultrasensitive RNA mutation analysis, enabling several basic research and clinical applications.

Counting unique molecular identifiers in sequencing using a multi-type branching process with immigration

Detection of extremely rare variant alleles, such as tumor DNA, within a complex mixture of DNA molecules is experimentally challenging due to sequencing errors. Barcoding of target DNA molecules in library construction for next-generation sequencing provides a way to identify and bioinformatically remove polymerase induced errors. During the barcoding procedure involving t consecutive PCR cycles, the DNA molecules become barcoded by Unique Molecular Identifiers (UMIs). Different library construction protocols utilize different values of t. The effect of a larger t and imperfect PCR amplifications in relation to UMI cluster sizes is poorly described. This paper proposes a branching process with growing immigration as a model describing the random outcome of t cycles of PCR barcoding. Our model discriminates between five different amplification rates r₁, r₂, r₃, r₄, r for different types of molecules associated with the PCR barcoding procedure. We study this model by focussing on C_t, the number of clusters of molecules sharing the same UMI, as well as C_t(m), the number of UMI clusters of size m. Our main finding is a remarkable asymptotic pattern valid for moderately large t. It turns out that E(C_t(m))/E(C_t)≈2^-m for m=1,2,…, regardless of the underlying parameters (r₁,r₂,r₃,r₄,r). The knowledge of the quantities C_t and C_t(m) as functions of the experimental parameters t and (r₁,r₂,r₃,r₄,r) will help the users to draw more adequate conclusions from the outcomes of different sequencing protocols.

Exploration of shared features of B cell receptor and T cell receptor repertoires reveals distinct clonotype clusters

Although B cells and T cells are integral players of the adaptive immune system and act in co-dependent ways to orchestrate immune responses, existing methods to study the immune repertoire have largely focused on separate analyses of B cell receptor (BCR) and T cell receptor (TCR) repertoires. Based on our hypothesis that the shared history of immune exposures and the shared cellular machinery for recombination result in similarities between BCR and TCR repertoires in an individual, we examine any commonalities and interrelationships between BCR and TCR repertoires. We find that the BCR and TCR repertoires have covarying clonal architecture and diversity, and that the pattern of correlations appears to be altered in immune-mediated diseases. Furthermore, hierarchical clustering of public B and T cell clonotypes in both health and disease based on correlation of clonal proportion revealed distinct clusters of B and T cell clonotypes that exhibit increased sequence similarity, share motifs, and have distinct amino acid characteristics. Our findings point to common principles governing memory formation, recombination, and clonal expansion to antigens in B and T cells within an individual. A significant proportion of public BCR and TCR repertoire can be clustered into nonoverlapping and correlated clusters, suggesting a novel way of grouping B and T cell clonotypes.

UMIErrorCorrect and UMIAnalyzer: Software for Consensus Read Generation, Error Correction, and Visualization Using Unique Molecular Identifiers

BACKGROUND

Targeted sequencing using unique molecular identifiers (UMIs) enables detection of rare variant alleles in challenging applications, such as cell-free DNA analysis from liquid biopsies. Standard bioinformatics pipelines for data processing and variant calling are not adapted for deep-sequencing data containing UMIs, are inflexible, and require multistep workflows or dedicated computing resources.

METHODS

We developed a bioinformatics pipeline using Python and an R package for data analysis and visualization. To validate our pipeline, we analyzed cell-free DNA reference material with known mutant allele frequencies (0%, 0.125%, 0.25%, and 1%) and public data sets.

RESULTS

We developed UMIErrorCorrect, a bioinformatics pipeline for analyzing sequencing data containing UMIs. UMIErrorCorrect only requires fastq files as inputs and performs alignment, UMI clustering, error correction, and variant calling. We also provide UMIAnalyzer, a graphical user interface, for data mining, visualization, variant interpretation, and report generation. UMIAnalyzer allows the user to adjust analysis parameters and study their effect on variant calling. We demonstrated the flexibility of UMIErrorCorrect by analyzing data from 4 different targeted sequencing protocols. We also show its ability to detect different mutant allele frequencies in standardized cell-free DNA reference material. UMIErrorCorrect outperformed existing pipelines for targeted UMI sequencing data in terms of variant detection sensitivity.

CONCLUSIONS

UMIErrorCorrect and UMIAnalyzer are comprehensive and customizable bioinformatics tools that can be applied to any type of library preparation protocol and enrichment chemistry using UMIs. Access to simple, generic, and open-source bioinformatics tools will facilitate the implementation of UMI-based sequencing approaches in basic research and clinical applications.

Defining TCRγδ lymphoproliferative disorders by combined immunophenotypic and molecular evaluation

Tγδ large granular lymphocyte leukemia (Tγδ LGLL) is a rare lymphoproliferative disease, scantily described in literature. A deep-analysis, in an initial cohort of 9 Tγδ LGLL compared to 23 healthy controls, shows that Tγδ LGLL dominant clonotypes are mainly public and exhibit different V-(D)-J γ/δ usage between patients with symptomatic and indolent Tγδ neoplasm. Moreover, some clonotypes share the same rearranged sequence. Data obtained in an enlarged cohort (n = 36) indicate the importance of a combined evaluation of immunophenotype and STAT mutational profile for the correct management of patients with Tγδ cell expansions. In fact, we observe an association between Vδ2/Vγ9 clonality and indolent course, while Vδ2/Vγ9 negativity correlates with symptomatic disease. Moreover, the 7 patients with STAT3 mutations have neutropenia and a CD56-/Vδ2- phenotype, and the 3 cases with STAT5B mutations display an asymptomatic clinical course and CD56/Vδ2 expression. All these data indicate that biological characterization is needed for Tγδ-cell neoplasm definition.

Tγδ large granular lymphocyte leukemia (Tγδ LGLL) is a rare lymphoproliferative neoplasm characterized by the expansion of T large granular lymphocytes expressing γδ TCR. Here, based on deep sequencing analysis of the clonotype repertoire, the authors show that leukemic Tγδ cells are characterized by recurrent public clonotypes that are diversified between symptomatic and asymptomatic patients.

Dual UMIs and Dual Barcodes With Minimal PCR Amplification Removes Artifacts and Acquires Accurate Antibody Repertoire

Antibody repertoire sequencing (Rep-seq) has been widely used to reveal repertoire dynamics and to interrogate antibodies of interest at single nucleotide-level resolution. However, polymerase chain reaction (PCR) amplification introduces extensive artifacts including chimeras and nucleotide errors, leading to false discovery of antibodies and incorrect assessment of somatic hypermutations (SHMs) which subsequently mislead downstream investigations. Here, a novel approach named DUMPArts, which improves the accuracy of antibody repertoires by labeling each sample with dual barcodes and each molecule with dual unique molecular identifiers (UMIs) via minimal PCR amplification to remove artifacts, is developed. Tested by ultra-deep Rep-seq data, DUMPArts removed inter-sample chimeras, which cause artifactual shared clones and constitute approximately 15% of reads in the library, as well as intra-sample chimeras with erroneous SHMs and constituting approximately 20% of the reads, and corrected base errors and amplification biases by consensus building. The removal of these artifacts will provide an accurate assessment of antibody repertoires and benefit related studies, especially mAb discovery and antibody-guided vaccine design.

CloneRetriever: An Automated Algorithm to Identify Clonal B and T Cell Gene Rearrangements by Next-Generation Sequencing for the Diagnosis of Lymphoid Malignancies

BACKGROUND

Clonal immunoglobulin and T-cell receptor rearrangements serve as tumor-specific markers that have become mainstays of the diagnosis and monitoring of lymphoid malignancy. Next-generation sequencing (NGS) techniques targeting these loci have been successfully applied to lymphoblastic leukemia and multiple myeloma for minimal residual disease detection. However, adoption of NGS for primary diagnosis remains limited.

METHODS

We addressed the bioinformatics challenges associated with immune cell sequencing and clone detection by designing a novel web tool, CloneRetriever (CR), which uses machine-learning principles to generate clone classification schemes that are customizable, and can be applied to large datasets. CR has 2 applications-a "validation" mode to derive a clonality classifier, and a "live" mode to screen for clones by applying a validated and/or customized classifier. In this study, CR-generated multiple classifiers using 2 datasets comprising 106 annotated patient samples. A custom classifier was then applied to 36 unannotated samples.

RESULTS

The optimal classifier for clonality required clonal dominance ≥4.5× above background, read representation ≥8% of all reads, and technical replicate agreement. Depending on the dataset and analysis step, the optimal algorithm yielded sensitivities of 81%-90%, specificities of 97%-100%, areas under the curve of 91%-94%, positive predictive values of 92-100%, and negative predictive values of 88%-98%. Customization of the algorithms yielded 95%-100% concordance with gold-standard clonality determination, including rescue of indeterminate samples. Application to a set of unknowns showed concordance rates of 83%-96%.

CONCLUSIONS

CR is an out-of-the-box ready and user-friendly software designed to identify clonal rearrangements in large NGS datasets for the diagnosis of lymphoid malignancies.

Ultrasensitive circulating tumor DNA analysis enables precision medicine: experimental workflow considerations

Introduction: Circulating tumor DNA (ctDNA) has become a relevant biomarker in cancer management, allowing tumor assessment through analysis of minimally invasive liquid biopsies. Applications include screening, diagnostics, monitoring of treatment efficacy and detection of minimal residual disease as well as relapse. The potential of ctDNA analysis is significant, but several biological and technical challenges need to be addressed before widespread clinical implementation.Areas covered: Several clinical applications where ctDNA analysis may be beneficial require detection of individual DNA molecules. Consequently, to acquire accurate and informative data the entire workflow from sampling to final data interpretation needs to be optimized. In this review, we discuss the biological and technical challenges of ctDNA analysis and how preanalytical and analytical approaches affect different cancer applications.Expert opinion: While numerous studies have demonstrated the potential of using ctDNA in cancer applications, yet few reports about true clinical utility exist. Despite encouraging data, the sensitivity of ctDNA analyses, i.e. the probability to detect presence of cancer in liquid biopsies, is still an issue. Analysis of multiple mutations in combination with simultaneous assessment of other analytes is one solution. Improved standardization and guidelines will also facilitate the introduction of ctDNA analysis into clinical routine.

MAUI-seq: Metabarcoding using amplicons with unique molecular identifiers to improve error correction

Sequencing and PCR errors are a major challenge when characterizing genetic diversity using high-throughput amplicon sequencing (HTAS). We have developed a multiplexed HTAS method, MAUI-seq, which uses unique molecular identifiers (UMIs) to improve error correction by exploiting variation among sequences associated with a single UMI. Erroneous sequences are recognized because, across the data set, they are over-represented among the minor sequences associated with UMIs. We show that two main advantages of this approach are efficient elimination of chimeric and other erroneous reads, outperforming dada2 and unoise3, and the ability to confidently recognize genuine alleles that are present at low abundance or resemble chimeras. The method provides sensitive and flexible profiling of diversity and is readily adaptable to most HTAS applications, including microbial 16S rRNA profiling and metabarcoding of environmental DNA.