Statistical inference of the generation probability of T-cell receptors from sequence repertoires

The quantum model of T-cell activation: Revisiting immune response theories

Our understanding of the immune response is far from complete, missing out on more detailed explanations that could be provided by molecular insights. To bridge this gap, we introduce the quantum model of T-cell activation. This model suggests that the transfer of energy during protein phosphorylation within T cells is not a continuous flow but occurs in discrete bursts, or 'quanta', of phosphates. This quantized energy transfer is mediated by oscillating cycles of receptor phosphorylation and dephosphorylation, initiated by dynamic 'catch-slip' pulses in the peptide-major histocompatibility complex-T-cell receptor (pMHC-TcR) interactions. T-cell activation is predicated upon achieving a critical threshold of catch-slip pulses at the pMHC-TcR interface. Costimulation is relegated to a secondary role, becoming crucial only when the frequency of pMHC-TcR catch-slip pulses does not meet the necessary threshold for this quanta-based energy transfer. Therefore, our model posits that it is the quantum nature of energy transfer-not the traditional signal I or signal II-that plays the decisive role in T-cell activation. This paradigm shift highlights the importance of understanding T-cell activation through a quantum lens, offering a potentially transformative perspective on immune response regulation.

RNA vaccines for cancer: Principles to practice

Vaccines are the most impactful medicines to improve health. Though potent against pathogens, vaccines for cancer remain an unfulfilled promise. However, recent advances in RNA technology coupled with scientific and clinical breakthroughs have spurred rapid discovery and potent delivery of tumor antigens at speed and scale, transforming cancer vaccines into a tantalizing prospect. Yet, despite being at a pivotal juncture, with several randomized clinical trials maturing in upcoming years, several critical questions remain: which antigens, tumors, platforms, and hosts can trigger potent immunity with clinical impact? Here, we address these questions with a principled framework of cancer vaccination from antigen detection to delivery. With this framework, we outline features of emergent RNA technology that enable rapid, robust, real-time vaccination with somatic mutation-derived neoantigens-an emerging "ideal" antigen class-and highlight latent features that have sparked the belief that RNA could realize the enduring vision for vaccines against cancer.

Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells

T cells have the ability to eliminate infected and cancer cells and play an essential role in cancer immunotherapy. T cell activation is elicited by the binding of the T cell receptor (TCR) to epitopes displayed on MHC molecules, and the TCR specificity is determined by the sequence of its α and β chains. Here, we collect and curate a dataset of 17,715 αβTCRs interacting with dozens of class I and class II epitopes. We use this curated data to develop MixTCRpred, an epitope-specific TCR-epitope interaction predictor. MixTCRpred accurately predicts TCRs recognizing several viral and cancer epitopes. MixTCRpred further provides a useful quality control tool for multiplexed single-cell TCR sequencing assays of epitope-specific T cells and pinpoints a substantial fraction of putative contaminants in public databases. Analysis of epitope-specific dual α T cells demonstrates that MixTCRpred can identify α chains mediating epitope recognition. Applying MixTCRpred to TCR repertoires from COVID-19 patients reveals enrichment of clonotypes predicted to bind an immunodominant SARS-CoV-2 epitope. Overall, MixTCRpred provides a robust tool to predict TCRs interacting with specific epitopes and interpret TCR-sequencing data from both bulk and epitope-specific T cells.

Detecting T-cell clonal expansions and quantifying clone survival using deep profiling of immune repertoires

An individual's T-cell repertoire constantly changes under the influence of external and internal factors. Cells that do not receive a stimulatory signal die, while those that encounter and recognize a pathogen or receive a co-stimulatory signal divide, resulting in clonal expansions. T-cell clones can be traced by monitoring the presence of their unique T-cell receptor (TCR) sequence, which is assembled de novo through a process known as V(D)J rearrangement. Tracking T cells can provide valuable insights into the survival of cells after hematopoietic stem cell transplantation (HSCT) or cancer treatment response and can indicate the induction of protective immunity by vaccination. In this study, we report a bioinformatic method for quantifying the T-cell repertoire dynamics from TCR sequencing data. We demonstrate its utility by measuring the T-cell repertoire stability in healthy donors, by quantifying the effect of donor lymphocyte infusion (DLI), and by tracking the fate of the different T-cell subsets in HSCT patients and the expansion of pathogen-specific clones in vaccinated individuals.

Innate-like T cell subset commitment in the murine thymus is independent of TCR characteristics and occurs during proliferation

How T-cell receptor (TCR) characteristics determine subset commitment during T-cell development is still unclear. Here, we addressed this question for innate-like T cells, mucosal-associated invariant T (MAIT) cells, and invariant natural killer T (iNKT) cells. MAIT and iNKT cells have similar developmental paths, leading in mice to two effector subsets, cytotoxic (MAIT1/iNKT1) and IL17-secreting (MAIT17/iNKT17). For iNKT1 vs iNKT17 fate choice, an instructive role for TCR affinity was proposed but recent data argue against this model. Herein, we examined TCR role in MAIT and iNKT subset commitment through scRNAseq and TCR repertoire analysis. In our dataset of thymic MAIT cells, we found pairs of T-cell clones with identical amino acid TCR sequences originating from distinct precursors, one of which committed to MAIT1 and the other to MAIT17 fates. Quantitative in silico simulations indicated that the number of such cases is best explained by lineage choice being independent of TCR characteristics. Comparison of TCR features of MAIT1 and MAIT17 clonotypes demonstrated that the subsets cannot be distinguished based on TCR sequence. To pinpoint the developmental stage associated with MAIT sublineage choice, we demonstrated that proliferation takes place both before and after MAIT fate commitment. Altogether, we propose a model of MAIT cell development in which noncommitted, intermediate-stage MAIT cells undergo a first round of proliferation, followed by TCR characteristics-independent commitment to MAIT1 or MAIT17 lineage, followed by an additional round of proliferation. Reanalyzing a published iNKT TCR dataset, we showed that this model is also relevant for iNKT cell development.

How persistent infection overcomes peripheral tolerance mechanisms to cause T cell-mediated autoimmune disease

T cells help orchestrate immune responses to pathogens, and their aberrant regulation can trigger autoimmunity. Recent studies highlight that a threshold number of T cells (a quorum) must be activated in a tissue to mount a functional immune response. These collective effects allow the T cell repertoire to respond to pathogens while suppressing autoimmunity due to circulating autoreactive T cells. Our computational studies show that increasing numbers of pathogenic peptides targeted by T cells during persistent or severe viral infections increase the probability of activating T cells that are weakly reactive to self-antigens (molecular mimicry). These T cells are easily re-activated by the self-antigens and contribute to exceeding the quorum threshold required to mount autoimmune responses. Rare peptides that activate many T cells are sampled more readily during severe/persistent infections than in acute infections, which amplifies these effects. Experiments in mice to test predictions from these mechanistic insights are suggested.

Chronic infection control relies on T cells with lower foreign antigen binding strength generated by N-nucleotide diversity

The breadth of pathogens to which T cells can respond is determined by the T cell receptors (TCRs) present in an individual's repertoire. Although more than 90% of the sequence diversity among TCRs is generated by terminal deoxynucleotidyl transferase (TdT)-mediated N-nucleotide addition during V(D)J recombination, the benefit of TdT-altered TCRs remains unclear. Here, we computationally and experimentally investigated whether TCRs with higher N-nucleotide diversity via TdT make distinct contributions to acute or chronic pathogen control specifically through the inclusion of TCRs with lower antigen binding strengths (i.e., lower reactivity to peptide-major histocompatibility complex (pMHC)). When T cells with high pMHC reactivity have a greater propensity to become functionally exhausted than those of low pMHC reactivity, our computational model predicts a shift toward T cells with low pMHC reactivity over time during chronic, but not acute, infections. This TCR-affinity shift is critical, as the elimination of T cells with lower pMHC reactivity in silico substantially increased the time to clear a chronic infection, while acute infection control remained largely unchanged. Corroborating an affinity-centric benefit for TCR diversification via TdT, we found evidence that TdT-deficient TCR repertoires possess fewer T cells with weaker pMHC binding strengths in vivo and showed that TdT-deficient mice infected with a chronic, but not an acute, viral pathogen led to protracted viral clearance. In contrast, in the case of a chronic fungal pathogen where T cells fail to clear the infection, both our computational model and experimental data showed that TdT-diversified TCR repertoires conferred no additional protection to the hosts. Taken together, our in silico and in vivo data suggest that TdT-mediated TCR diversity is of particular benefit for the eventual resolution of prolonged pathogen replication through the inclusion of TCRs with lower foreign antigen binding strengths.

Protective fibroblastic niches in secondary lymphoid organs

Fibroblastic reticular cells (FRCs) are specialized fibroblasts of secondary lymphoid organs that provide the structural foundation of the tissue. Moreover, FRCs guide immune cells to dedicated microenvironmental niches where they provide lymphocytes and myeloid cells with homeostatic growth and differentiation factors. Inflammatory processes, including infection with pathogens, induce rapid morphological and functional adaptations that are critical for the priming and regulation of protective immune responses. However, adverse FRC reprogramming can promote immunopathological tissue damage during infection and autoimmune conditions and subvert antitumor immune responses. Here, we review recent findings on molecular pathways that regulate FRC-immune cell crosstalk in specialized niches during the generation of protective immune responses in the course of pathogen encounters. In addition, we discuss how FRCs integrate immune cell-derived signals to ensure protective immunity during infection and how therapies for inflammatory diseases and cancer can be developed through improved understanding of FRC-immune cell interactions.

Machine learning analysis of the T cell receptor repertoire identifies sequence features of self-reactivity

The T cell receptor (TCR) determines specificity and affinity for both foreign and self-peptides presented by the major histocompatibility complex (MHC). Although the strength of TCR interactions with self-pMHC impacts T cell function, it has been challenging to identify TCR sequence features that predict T cell fate. To discern patterns distinguishing TCRs from naive CD4+ T cells with low versus high self-reactivity, we used data from 42 mice to train a machine learning (ML) algorithm that identifies population-level differences between TCRβ sequence sets. This approach revealed that weakly self-reactive T cell populations were enriched for longer CDR3β regions and acidic amino acids. We tested our ML predictions of self-reactivity using retrogenic mice with fixed TCRβ sequences. Extrapolating our analyses to independent datasets, we predicted high self-reactivity for regulatory T cells and slightly reduced self-reactivity for T cells responding to chronic infections. Our analyses suggest a potential trade-off between TCR repertoire diversity and self-reactivity. A record of this paper's transparent peer review process is included in the supplemental information.

Multi-variate model of T cell clonotype competition and homeostasis

Diversity of the naive T cell repertoire is maintained by competition for stimuli provided by self-peptides bound to major histocompatibility complexes (self-pMHCs). We extend an existing bi-variate competition model to a multi-variate model of the dynamics of multiple T cell clonotypes which share stimuli. In order to understand the late-time behaviour of the system, we analyse: (i) the dynamics until the extinction of the first clonotype, (ii) the time to the first extinction event, (iii) the probability of extinction of each clonotype, and (iv) the size of the surviving clonotypes when the first extinction event takes place. We also find the probability distribution of the number of cell divisions per clonotype before its extinction. The mean size of a new clonotype at quasi-steady state is an increasing function of the stimulus available to it, and a decreasing function of the fraction of stimuli it shares with other clonotypes. Thus, the probability of, and time to, extinction of a new clonotype entering the pool of T cell clonotypes is determined by the extent of competition for stimuli it experiences and by its initial number of cells.

Tracking DNA-based antigen-specific T cell receptors during progression to type 1 diabetes

T cells targeting self-proteins are important mediators in autoimmune diseases. T cells express unique cell-surface receptors (TCRs) that recognize peptides presented by major histocompatibility molecules. TCRs have been identified from blood and pancreatic islets of individuals with type 1 diabetes (T1D). Here, we tracked ~1700 known antigen-specific TCR sequences, islet antigen or viral reactive, in bulk TCRβ sequencing from longitudinal blood DNA samples in at-risk cases who progressed to T1D, age/sex/human leukocyte antigen-matched controls, and a new-onset T1D cohort. Shared and frequent antigen-specific TCRβ sequences were identified in all three cohorts, and viral sequences were present across all ages. Islet sequences had different patterns of accumulation based upon antigen specificity in the at-risk cases. Furthermore, 73 islet-antigen TCRβ sequences were present in higher frequencies and numbers in T1D samples relative to controls. The total number of these disease-associated TCRβ sequences inversely correlated with age at clinical diagnosis, indicating the potential to use disease-relevant TCR sequences as biomarkers in autoimmune disorders.

Neural network models for sequence-based TCR and HLA association prediction

T cells rely on their T cell receptors (TCRs) to discern foreign antigens presented by human leukocyte antigen (HLA) proteins. The TCRs of an individual contain a record of this individual's past immune activities, such as immune response to infections or vaccines. Mining the TCR data may recover useful information or biomarkers for immune related diseases or conditions. Some TCRs are observed only in the individuals with certain HLA alleles, and thus characterizing TCRs requires a thorough understanding of TCR-HLA associations. The extensive diversity of HLA alleles and the rareness of some HLA alleles present a formidable challenge for this task. Existing methods either treat HLA as a categorical variable or represent an HLA by its alphanumeric name, and have limited ability to generalize to the HLAs that are not seen in the training process. To address this challenge, we propose a neural network-based method named Deep learning Prediction of TCR-HLA association (DePTH) to predict TCR-HLA associations based on their amino acid sequences. We demonstrate that DePTH is capable of making reasonable predictions for TCR-HLA associations, even when neither the HLA nor the TCR have been included in the training dataset. Furthermore, we establish that DePTH can be used to quantify the functional similarities among HLA alleles, and that these HLA similarities are associated with the survival outcomes of cancer patients who received immune checkpoint blockade treatments.

Conserved biophysical compatibility among the highly variable germline-encoded regions shapes TCR-MHC interactions

T cells are critically important components of the adaptive immune system primarily responsible for identifying and responding to pathogenic challenges. This recognition of pathogens is driven by the interaction between membrane-bound T cell receptors (TCRs) and antigenic peptides presented on major histocompatibility complex (MHC) molecules. The formation of the TCR-peptide-MHC complex (TCR-pMHC) involves interactions among germline-encoded and hypervariable amino acids. Germline-encoded and hypervariable regions can form contacts critical for complex formation, but only interactions between germline-encoded contacts are likely to be shared across many of all the possible productive TCR-pMHC complexes. Despite this, experimental investigation of these interactions have focused on only a small fraction of the possible interaction space. To address this, we analyzed every possible germline-encoded TCR-MHC contact in humans, thereby generating the first comprehensive characterization of these largely antigen-independent interactions. Our computational analysis suggests that germline-encoded TCR-MHC interactions that are conserved at the sequence level are rare due to the high amino acid diversity of the TCR CDR1 and CDR2 loops, and that such conservation is unlikely to dominate the dynamic protein-protein binding interface. Instead, we propose that binding properties such as the docking orientation are defined by regions of biophysical compatibility between these loops and the MHC surface.

Mathematical Characterization of Private and Public Immune Receptor Sequences

Diverse T and B cell repertoires play an important role in mounting effective immune responses against a wide range of pathogens and malignant cells. The number of unique T and B cell clones is characterized by T and B cell receptors (TCRs and BCRs), respectively. Although receptor sequences are generated probabilistically by recombination processes, clinical studies found a high degree of sharing of TCRs and BCRs among different individuals. In this work, we use a general probabilistic model for T/B cell receptor clone abundances to define "publicness" or "privateness" and information-theoretic measures for comparing the frequency of sampled sequences observed across different individuals. We derive mathematical formulae to quantify the mean and the variances of clone richness and overlap. Our results can be used to evaluate the effect of different sampling protocols on abundances of clones within an individual as well as the commonality of clones across individuals. Using synthetic and empirical TCR amino acid sequence data, we perform simulations to study expected clonal commonalities across multiple individuals. Based on our formulae, we compare these simulated results with the analytically predicted mean and variances of the repertoire overlap. Complementing the results on simulated repertoires, we derive explicit expressions for the richness and its uncertainty for specific, single-parameter truncated power-law probability distributions. Finally, the information loss associated with grouping together certain receptor sequences, as is done in spectratyping, is also evaluated. Our approach can be, in principle, applied under more general and mechanistically realistic clone generation models.

Numbers and odds: TCR repertoire size and its age changes impacting on T cell functions

A vast array of αβ T cell receptors (TCRs) is generated during T cell development in the thymus through V(D)J recombination, which involves the rearrangement of multiple V, D, and J genes and the pairing of α and β chains. These diverse TCRs provide protection to the human body against a multitude of foreign pathogens and internal cancer cells. The entirety of TCRs present in an individual's T cells is referred to as the TCR repertoire. Despite an estimated 4 × 1011 T cells in the adult human body, the lower bound estimate for the TCR repertoire is 3.8 × 108. While the number of circulating T cells may slightly decrease with age, the changes in the diversity of the TCR repertoire is more apparent. Here, I review recent advancements in TCR repertoire studies, the methods used to measure it, how richness and diversity change as humans age, and some of the known consequences associated with these changes.

NAIR: Network Analysis of Immune Repertoire

T cells represent a crucial component of the adaptive immune system and mediate anti-tumoral immunity as well as protection against infections, including respiratory viruses such as SARS-CoV-2. Next-generation sequencing of the T-cell receptors (TCRs) can be used to profile the T-cell repertoire. We developed a customized pipeline for Network Analysis of Immune Repertoire (NAIR) with advanced statistical methods to characterize and investigate changes in the landscape of TCR sequences. We first performed network analysis on the TCR sequence data based on sequence similarity. We then quantified the repertoire network by network properties and correlated it with clinical outcomes of interest. In addition, we identified (1) disease-specific/associated clusters and (2) shared clusters across samples based on our customized search algorithms and assessed their relationship with clinical outcomes such as recovery from COVID-19 infection. Furthermore, to identify disease-specific TCRs, we introduced a new metric that incorporates the clonal generation probability and the clonal abundance by using the Bayes factor to filter out the false positives. TCR-seq data from COVID-19 subjects and healthy donors were used to illustrate that the proposed approach to analyzing the network architecture of the immune repertoire can reveal potential disease-specific TCRs responsible for the immune response to infection.

A novel approach to T-cell receptor beta chain (TCRB) repertoire encoding using lossless string compression

MOTIVATION

T-cell receptor beta chain (TCRB) repertoires are crucial for understanding immune responses. However, their high diversity and complexity present significant challenges in representation and analysis. The main motivation of this study is to develop a unified and compact representation of a TCRB repertoire that can efficiently capture its inherent complexity and diversity and allow for direct inference.

RESULTS

We introduce a novel approach to TCRB repertoire encoding and analysis, leveraging the Lempel-Ziv 76 algorithm. This approach allows us to create a graph-like model, identify-specific sequence features, and produce a new encoding approach for an individual's repertoire. The proposed representation enables various applications, including generation probability inference, informative feature vector derivation, sequence generation, a new measure for diversity estimation, and a new sequence centrality measure. The approach was applied to four large-scale public TCRB sequencing datasets, demonstrating its potential for a wide range of applications in big biological sequencing data.

AVAILABILITY AND IMPLEMENTATION

Python package for implementation is available https://github.com/MuteJester/LZGraphs.

Origin and evolutionary malleability of T cell receptor α diversity

Lymphocytes of vertebrate adaptive immune systems acquired the capability to assemble, from split genes in the germline, billions of functional antigen receptors^1-3. These receptors show specificity; unlike the broadly tuned receptors of the innate system, antibodies (Ig) expressed by B cells, for instance, can accurately distinguish between the two enantiomers of organic acids⁴, whereas T cell receptors (TCRs) reliably recognize single amino acid replacements in their peptide antigens⁵. In developing lymphocytes, antigen receptor genes are assembled from a comparatively small set of germline-encoded genetic elements in a process referred to as V(D)J recombination^6,7. Potential self-reactivity of some antigen receptors arising from the quasi-random somatic diversification is suppressed by several robust control mechanisms^8-12. For decades, scientists have puzzled over the evolutionary origin of somatically diversifying antigen receptors^13-16. It has remained unclear how, at the inception of this mechanism, immunologically beneficial expanded receptor diversity was traded against the emerging risk of destructive self-recognition. Here we explore the hypothesis that in early vertebrates, sequence microhomologies marking the ends of recombining elements became the crucial targets of selection determining the outcome of non-homologous end joining-based repair of DNA double-strand breaks generated during RAG-mediated recombination. We find that, across the main clades of jawed vertebrates, TCRα repertoire diversity is best explained by species-specific extents of such sequence microhomologies. Thus, selection of germline sequence composition of rearranging elements emerges as a major factor determining the degree of diversity of somatically generated antigen receptors.

Large clones of pre-existing T cells drive early immunity against SARS-COV-2 and LCMV infection

T cell responses precede antibody and may provide early control of infection. We analyzed the clonal basis of this rapid response following SARS-COV-2 infection. We applied T cell receptor (TCR) sequencing to define the trajectories of individual T cell clones immediately. In SARS-COV-2 PCR+ individuals, a wave of TCRs strongly but transiently expand, frequently peaking the same week as the first positive PCR test. These expanding TCR CDR3s were enriched for sequences functionally annotated as SARS-COV-2 specific. Epitopes recognized by the expanding TCRs were highly conserved between SARS-COV-2 strains but not with circulating human coronaviruses. Many expanding CDR3s were present at high frequency in pre-pandemic repertoires. Early response TCRs specific for lymphocytic choriomeningitis virus epitopes were also found at high frequency in the preinfection naive repertoire. High-frequency naive precursors may allow the T cell response to respond rapidly during the crucial early phases of acute viral infection.

Neural Network Models for Sequence-Based TCR and HLA Association Prediction

T cells rely on their T cell receptors (TCRs) to recognize foreign antigens presented by human leukocyte antigen (HLA) proteins. TCRs contain a record of an individual's past immune activities, and some TCRs are observed only in individuals with certain HLA alleles. As a result, characterising TCRs requires a thorough understanding of TCR-HLA associations. To this end, we propose a neural network method named Deep learning Prediction of TCR-HLA association (DePTH) to predict TCR-HLA associations based on their amino acid sequences. We show that the DePTH can be used to quantify the functional similarities of HLA alleles, and that these HLA similarities are associated with the survival outcomes of cancer patients who received immune checkpoint blockade treatment.

Statistical inference reveals the role of length, GC content, and local sequence in V(D)J nucleotide trimming

To appropriately defend against a wide array of pathogens, humans somatically generate highly diverse repertoires of B cell and T cell receptors (BCRs and TCRs) through a random process called V(D)J recombination. Receptor diversity is achieved during this process through both the combinatorial assembly of V(D)J-genes and the junctional deletion and insertion of nucleotides. While the Artemis protein is often regarded as the main nuclease involved in V(D)J recombination, the exact mechanism of nucleotide trimming is not understood. Using a previously published TCRβ repertoire sequencing data set, we have designed a flexible probabilistic model of nucleotide trimming that allows us to explore various mechanistically interpretable sequence-level features. We show that local sequence context, length, and GC nucleotide content in both directions of the wider sequence, together, can most accurately predict the trimming probabilities of a given V-gene sequence. Because GC nucleotide content is predictive of sequence-breathing, this model provides quantitative statistical evidence regarding the extent to which double-stranded DNA may need to be able to breathe for trimming to occur. We also see evidence of a sequence motif that appears to get preferentially trimmed, independent of GC-content-related effects. Further, we find that the inferred coefficients from this model provide accurate prediction for V- and J-gene sequences from other adaptive immune receptor loci. These results refine our understanding of how the Artemis nuclease may function to trim nucleotides during V(D)J recombination and provide another step toward understanding how V(D)J recombination generates diverse receptors and supports a powerful, unique immune response in healthy humans.

Deep autoregressive generative models capture the intrinsics embedded in T-cell receptor repertoires

T-cell receptors (TCRs) play an essential role in the adaptive immune system. Probabilistic models for TCR repertoires can help decipher the underlying complex sequence patterns and provide novel insights into understanding the adaptive immune system. In this work, we develop TCRpeg, a deep autoregressive generative model to unravel the sequence patterns of TCR repertoires. TCRpeg largely outperforms state-of-the-art methods in estimating the probability distribution of a TCR repertoire, boosting the average accuracy from 0.672 to 0.906 measured by the Pearson correlation coefficient. Furthermore, with promising performance in probability inference, TCRpeg improves on a range of TCR-related tasks: profiling TCR repertoire probabilistically, classifying antigen-specific TCRs, validating previously discovered TCR motifs, generating novel TCRs and augmenting TCR data. Our results and analysis highlight the flexibility and capacity of TCRpeg to extract TCR sequence information, providing a novel approach for deciphering complex immunogenomic repertoires.

Minimal/Measurable Residual Disease (MRD) Monitoring in Patients with Lymphoid Neoplasms by High-Throughput Sequencing of the T-Cell Receptor

High-throughput sequencing of the T-cell receptor beta (TRB) and gamma (TRG) loci is increasingly utilized due to its high sensitivity, specificity, and versatility in the diagnosis of various T-cell malignancies. Application of these technologies for tracking disease burden can be valuable in detecting recurrence, determining response to therapy, guiding future management of patients, and establishing endpoints for clinical trials. In this study, the performance of the commercially available LymphoTrack high-throughput sequencing assay was assessed for determining residual disease burden in patients with various T-cell malignancies seen at the authors' institution. A custom bioinformatics pipeline and database was also developed to facilitate minimal/measurable residual disease analysis and clinical reporting. This assay demonstrated excellent test performance characteristics, achieving a sensitivity of 1 of 100,000 T-cell equivalents for the DNA inputs evaluated and high concordance with orthogonal testing methods. This assay was further utilized to correlate disease burden in several patients, demonstrating its potential utility for monitoring patients with T-cell malignancies.

Measures of epitope binding degeneracy from T cell receptor repertoires

Adaptive immunity is driven by specific binding of hypervariable receptors to diverse molecular targets. The sequence diversity of receptors and targets are both individually known but because multiple receptors can recognize the same target, a measure of the effective "functional" diversity of the human immune system has remained elusive. Here, we show that sequence near-coincidences within T cell receptors that bind specific epitopes provide a new window into this problem and allow the quantification of how binding probability covaries with sequence. We find that near-coincidence statistics within epitope-specific repertoires imply a measure of binding degeneracy to amino acid changes in receptor sequence that is consistent across disparate experiments. Paired data on both chains of the heterodimeric receptor are particularly revealing since simultaneous near-coincidences are rare and we show how they can be exploited to estimate the number of epitope responses that created the memory compartment. In addition, we find that paired-chain coincidences are strongly suppressed across donors with different human leukocyte antigens, evidence for a central role of antigen-driven selection in making paired chain receptors public. These results demonstrate the power of coincidence analysis to reveal the sequence determinants of epitope binding in receptor repertoires.

The use of non-functional clonotypes as a natural calibrator for quantitative bias correction in adaptive immune receptor repertoire profiling

High-throughput sequencing of adaptive immune receptor repertoires is a valuable tool for receiving insights in adaptive immunity studies. Several powerful TCR/BCR repertoire reconstruction and analysis methods have been developed in the past decade. However, detecting and correcting the discrepancy between real and experimentally observed lymphocyte clone frequencies are still challenging. Here, we discovered a hallmark anomaly in the ratio between read count and clone count-based frequencies of non-functional clonotypes in multiplex PCR-based immune repertoires. Calculating this anomaly, we formulated a quantitative measure of V- and J-genes frequency bias driven by multiplex PCR during library preparation called Over Amplification Rate (OAR). Based on the OAR concept, we developed an original software for multiplex PCR-specific bias evaluation and correction named iROAR: immune Repertoire Over Amplification Removal (https://github.com/smiranast/iROAR). The iROAR algorithm was successfully tested on previously published TCR repertoires obtained using both 5' RACE (Rapid Amplification of cDNA Ends)-based and multiplex PCR-based approaches and compared with a biological spike-in-based method for PCR bias evaluation. The developed approach can increase the accuracy and consistency of repertoires reconstructed by different methods making them more applicable for comparative analysis.

Quantifying changes in the T cell receptor repertoire during thymic development

One of the feats of adaptive immunity is its ability to recognize foreign pathogens while sparing the self. During maturation in the thymus, T cells are selected through the binding properties of their antigen-specific T-cell receptor (TCR), through the elimination of both weakly (positive selection) and strongly (negative selection) self-reactive receptors. However, the impact of thymic selection on the TCR repertoire is poorly understood. Here, we use transgenic Nur77-mice expressing a T-cell activation reporter to study the repertoires of thymic T cells at various stages of their development, including cells that do not pass selection. We combine high-throughput repertoire sequencing with statistical inference techniques to characterize the selection of the TCR in these distinct subsets. We find small but significant differences in the TCR repertoire parameters between the maturation stages, which recapitulate known differentiation pathways leading to the CD4⁺ and CD8⁺ subtypes. These differences can be simulated by simple models of selection acting linearly on the sequence features. We find no evidence of specific sequences or sequence motifs or features that are suppressed by negative selection. These results favour a collective or statistical model for T-cell self non-self discrimination, where negative selection biases the repertoire away from self recognition, rather than ensuring lack of self-reactivity at the single-cell level.

What's self got to do with it: Sources of heterogeneity among naive T cells

There is a long-standing assumption that naive CD4⁺ and CD8⁺ T cells are largely homogeneous populations despite the extraordinary diversity of their T cell receptors (TCR). The self-immunopeptidome plays a key role in the selection of the naive T cell repertoire in the thymus, and self-peptides are also an important driver of differences between individual naive T cells with regard to their subsequent functional contributions to an immune response. Accumulating evidence suggests that as early as the β-selection stage of T cell development, when only one of the recombined chains of the mature TCR is expressed, signaling thresholds may be established for positive selection of immature thymocytes. Stochastic encounters subsequently made with self-ligands during positive selection in the thymus imprint functional biases that a T cell will carry with it throughout its lifetime, although ongoing interactions with self in the periphery ensure a level of plasticity in the gene expression wiring of naive T cells. Identifying the sources of heterogeneity in the naive T cell population and which functional attributes of T cells can be modulated through post-thymic interventions versus those that are fixed during T cell development, could enable us to better select or generate T cells with particular traits to improve the efficacy of T cell therapies.

simAIRR: simulation of adaptive immune repertoires with realistic receptor sequence sharing for benchmarking of immune state prediction methods

BACKGROUND

Machine learning (ML) has gained significant attention for classifying immune states in adaptive immune receptor repertoires (AIRRs) to support the advancement of immunodiagnostics and therapeutics. Simulated data are crucial for the rigorous benchmarking of AIRR-ML methods. Existing approaches to generating synthetic benchmarking datasets result in the generation of naive repertoires missing the key feature of many shared receptor sequences (selected for common antigens) found in antigen-experienced repertoires.

RESULTS

We demonstrate that a common approach to generating simulated AIRR benchmark datasets can introduce biases, which may be exploited for undesired shortcut learning by certain ML methods. To mitigate undesirable access to true signals in simulated AIRR datasets, we devised a simulation strategy (simAIRR) that constructs antigen-experienced-like repertoires with a realistic overlap of receptor sequences. simAIRR can be used for constructing AIRR-level benchmarks based on a range of assumptions (or experimental data sources) for what constitutes receptor-level immune signals. This includes the possibility of making or not making any prior assumptions regarding the similarity or commonality of immune state-associated sequences that will be used as true signals. We demonstrate the real-world realism of our proposed simulation approach by showing that basic ML strategies perform similarly on simAIRR-generated and real-world experimental AIRR datasets.

CONCLUSIONS

This study sheds light on the potential shortcut learning opportunities for ML methods that can arise with the state-of-the-art way of simulating AIRR datasets. simAIRR is available as a Python package: https://github.com/KanduriC/simAIRR.

Amino acids at position 5 in the peptide/MHC binding region of a public virus-specific TCR are completely inter-changeable without loss of function

Anti-viral T-cell responses are usually directed against a limited set of antigens, but often contain many T cells expressing different T-cell receptors (TCRs). Identical TCRs found within virus-specific T-cell populations in different individuals are known as public TCRs, but also TCRs highly-similar to these public TCRs, with only minor variations in amino acids on specific positions in the Complementary Determining Regions (CDRs), are frequently found. However, the degree of freedom at these positions was not clear. In this study, we used the HLA-A*02:01-restricted EBV-LMP2^FLY -specific public TCR as model and modified the highly-variable position 5 of the CDR3β sequence with all 20 amino acids. Our results demonstrate that amino acids at this particular position in the CDR3β region of this TCR are completely inter-changeable, without loss of TCR function. We show that the inability to find certain variants in individuals is explained by their lower recombination probability rather than by steric hindrance.

Comparative Study of Repertoire Classification Methods Reveals Data Efficiency of k-mer Feature Extraction

The repertoire of T cell receptors encodes various types of immunological information. Machine learning is indispensable for decoding such information from repertoire datasets measured by next-generation sequencing (NGS). In particular, the classification of repertoires is the most basic task, which is relevant for a variety of scientific and clinical problems. Supported by the recent appearance of large datasets, efficient but data-expensive methods have been proposed. However, it is unclear whether they can work efficiently when the available sample size is severely restricted as in practical situations. In this study, we demonstrate that their performances can be impaired substantially below critical sample sizes. To complement this drawback, we propose MotifBoost, which exploits the information of short k-mer motifs of TCRs. MotifBoost can perform the classification as efficiently as a deep learning method on large datasets while providing more stable and reliable results on small datasets. We tested MotifBoost on the four small datasets which consist of various conditions such as Cytomegalovirus (CMV), HIV, α-chain, β-chain and it consistently preserved the stability. We also clarify that the robustness of MotifBoost can be attributed to the efficiency of k-mer motifs as representation features of repertoires. Finally, by comparing the predictions of these methods, we show that the whole sequence identity and sequence motifs encode partially different information and that a combination of such complementary information is necessary for further development of repertoire analysis.

Machine Learning Approaches to TCR Repertoire Analysis

Sparked by the development of genome sequencing technology, the quantity and quality of data handled in immunological research have been changing dramatically. Various data and database platforms are now driving the rapid progress of machine learning for immunological data analysis. Of various topics in immunology, T cell receptor repertoire analysis is one of the most important targets of machine learning for assessing the state and abnormalities of immune systems. In this paper, we review recent repertoire analysis methods based on machine learning and deep learning and discuss their prospects.

γ9δ2 T-Cell Expansion and Phenotypic Profile Are Reflected in the CDR3δ Repertoire of Healthy Adults

γ9δ2T cells fill a distinct niche in human immunity due to the unique physiology of the phosphoantigen-reactive γ9δ2TCR. Here, we highlight reproducible TCRδ complementarity-determining region 3 (CDR3δ) repertoire patterns associated with γ9δ2T cell proliferation and phenotype, thus providing evidence for the role of the CDR3δ in modulating in vivo T-cell responses. Features that determine γ9δ2TCR binding affinity and reactivity to the phosphoantigen-induced ligand in vitro appear to similarly underpin in vivo clonotypic expansion and differentiation. Likewise, we identify a CDR3δ bias in the γ9δ2T cell natural killer receptor (NKR) landscape. While expression of the inhibitory receptor CD94/NKG2A is skewed toward cells bearing putative high-affinity TCRs, the activating receptor NKG2D is expressed independently of the phosphoantigen-sensing determinants, suggesting a higher net NKR activating signal in T cells with TCRs of low affinity. This study establishes consistent repertoire–phenotype associations and justifies stratification for the T-cell phenotype in future research on γ9δ2TCR repertoire dynamics.

Contact map dependence of a T-cell receptor binding repertoire

The T-cell arm of the adaptive immune system provides the host protection against unknown pathogens by discriminating between host and foreign material. This discriminatory capability is achieved by the creation of a repertoire of cells each carrying a T-cell receptor (TCR) specific to non-self-antigens displayed as peptides bound to the major histocompatibility complex (pMHC). The understanding of the dynamics of the adaptive immune system at a repertoire level is complex, due to both the nuanced interaction of a TCR-pMHC pair and to the number of different possible TCR-pMHC pairings, making computationally exact solutions currently unfeasible. To gain some insight into this problem, we study an affinity-based model for TCR-pMHC binding in which a crystal structure is used to generate a distance-based contact map that weights the pairwise amino acid interactions. We find that the TCR-pMHC binding energy distribution strongly depends both on the number of contacts and the repeat structure allowed by the topology of the contact map of choice; this in turn influences T-cell recognition probability during negative selection, with higher variances leading to higher survival probabilities. In addition, we quantify the degree to which neoantigens with mutations in sites with higher contacts are recognized at a higher rate.

Longitudinal analysis reveals age-related changes in the T cell receptor repertoire of human T cell subsets

A diverse T cell receptor (TCR) repertoire is essential for protection against a variety of pathogens, and TCR repertoire size is believed to decline with age. However, the precise size of human TCR repertoires, in both total and subsets of T cells, as well as their changes with age, are not fully characterized. We conducted a longitudinal analysis of the human blood TCRα and TCRβ repertoire of CD4⁺ and CD8⁺ T cell subsets using a unique molecular identifier–based (UMI-based) RNA-seq method. Thorough analysis of 1.9 × 10⁸ T cells yielded the lower estimate of TCR repertoire richness in an adult at 3.8 × 10⁸. Alterations of the TCR repertoire with age were observed in all 4 subsets of T cells. The greatest reduction was observed in naive CD8⁺ T cells, while the greatest clonal expansion was in memory CD8⁺ T cells, and the highest increased retention of TCR sequences was in memory CD8⁺ T cells. Our results demonstrated that age-related TCR repertoire attrition is subset specific and more profound for CD8⁺ than CD4⁺ T cells, suggesting that aging has a more profound effect on cytotoxic as opposed to helper T cell functions. This may explain the increased susceptibility of older adults to novel infections.

Learning the statistics and landscape of somatic mutation-induced insertions and deletions in antibodies

Affinity maturation is crucial for improving the binding affinity of antibodies to antigens. This process is mainly driven by point substitutions caused by somatic hypermutations of the immunoglobulin gene. It also includes deletions and insertions of genomic material known as indels. While the landscape of point substitutions has been extensively studied, a detailed statistical description of indels is still lacking. Here we present a probabilistic inference tool to learn the statistics of indels from repertoire sequencing data, which overcomes the pitfalls and biases of standard annotation methods. The model includes antibody-specific maturation ages to account for variable mutational loads in the repertoire. After validation on synthetic data, we applied our tool to a large dataset of human immunoglobulin heavy chains. The inferred model allows us to identify universal statistical features of indels in heavy chains. We report distinct insertion and deletion hotspots, and show that the distribution of lengths of indels follows a geometric distribution, which puts constraints on future mechanistic models of the hypermutation process.

Affinity maturation of B cell receptors is an important mechanism by which our body designs neutralizing antibodies to defend us against pathogens, including broadly neutralizing antibodies, which target a wide range of variants of the same pathogen. Such antibodies often contain key insertions and deletions to the germline gene, or “indels”, which are caused by somatic hypermutations. However, the mechanism, frequency and role of these indels are still elusive. We designed a computational method based on a probabilistic framework to infer the characteristics of this mutational process from high-throughput antibody sequencing experiments. Applied to human data, our approach provides a comprehensive quantitative description of insertions and deletions, opening avenues for better understanding the process of affinity maturation and the design of vaccines for eliciting a broad antibody response.

Public T-Cell Receptors (TCRs) Revisited by Analysis of the Magnitude of Identical and Highly-Similar TCRs in Virus-Specific T-Cell Repertoires of Healthy Individuals

Since multiple different T-cell receptor (TCR) sequences can bind to the same peptide-MHC combination and the number of TCR-sequences that can theoretically be generated even exceeds the number of T cells in a human body, the likelihood that many public identical (PUB-I) TCR-sequences frequently contribute to immune responses has been estimated to be low. Here, we quantitatively analyzed the TCR-repertoires of 190 purified virus-specific memory T-cell populations, directed against 21 epitopes of Cytomegalovirus, Epstein-Barr virus and Adenovirus isolated from 29 healthy individuals, and determined the magnitude, defined as prevalence within the population and frequencies within individuals, of PUB-I TCR and of TCR-sequences that are highly-similar (PUB-HS) to these PUB-I TCR-sequences. We found that almost one third of all TCR nucleotide-sequences represented PUB-I TCR amino-acid (AA) sequences and found an additional 12% of PUB-HS TCRs differing by maximally 3 AAs. We illustrate that these PUB-I and PUB-HS TCRs were structurally related and contained shared core-sequences in their TCR-sequences. We found a prevalence of PUB-I and PUB-HS TCRs of up to 50% among individuals and showed frequencies of virus-specific PUB-I and PUB-HS TCRs making up more than 10% of each virus-specific T-cell population. These findings were confirmed by using an independent TCR-database of virus-specific TCRs. We therefore conclude that the magnitude of the contribution of PUB-I and PUB-HS TCRs to these virus-specific T-cell responses is high. Because the T cells from these virus-specific memory TCR-repertoires were the result of successful control of the virus in these healthy individuals, these PUB-HS TCRs and PUB-I TCRs may be attractive candidates for immunotherapy in immunocompromised patients that lack virus-specific T cells to control viral reactivation.

Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities

Every T cell receptor (TCR) repertoire is shaped by a complex probabilistic tangle of genetically determined biases and immune exposures. T cells combine a random V(D)J recombination process with a selection process to generate highly diverse and functional TCRs. The extent to which an individual’s genetic background is associated with their resulting TCR repertoire diversity has yet to be fully explored. Using a previously published repertoire sequencing dataset paired with high-resolution genome-wide genotyping from a large human cohort, we infer specific genetic loci associated with V(D)J recombination probabilities using genome-wide association inference. We show that V(D)J gene usage profiles are associated with variation in the TCRB locus and, specifically for the functional TCR repertoire, variation in the major histocompatibility complex locus. Further, we identify specific variations in the genes encoding the Artemis protein and the TdT protein to be associated with biasing junctional nucleotide deletion and N-insertion, respectively. These results refine our understanding of genetically-determined TCR repertoire biases by confirming and extending previous studies on the genetic determinants of V(D)J gene usage and providing the first examples of trans genetic variants which are associated with modifying junctional diversity. Together, these insights lay the groundwork for further explorations into how immune responses vary between individuals.

Integrating T cell receptor sequences and transcriptional profiles by clonotype neighbor graph analysis (CoNGA)

Links between T cell clonotypes, as defined by T cell receptor (TCR) sequences, and phenotype, as reflected in gene expression (GEX) profiles, surface protein expression and peptide:major histocompatibility complex binding, can reveal functional relationships beyond the features shared by clonally related cells. Here we present clonotype neighbor graph analysis (CoNGA), a graph theoretic approach that identifies correlations between GEX profile and TCR sequence through statistical analysis of GEX and TCR similarity graphs. Using CoNGA, we uncovered associations between TCR sequence and GEX profiles that include a previously undescribed 'natural lymphocyte' population of human circulating CD8⁺ T cells and a set of TCR sequence determinants of differentiation in thymocytes. These examples show that CoNGA might help elucidate complex relationships between TCR sequence and T cell phenotype in large, heterogeneous, single-cell datasets.

OUP accepted manuscript

The study and manipulation of T cell receptors (TCRs) is central to multiple fields across basic and translational immunology research. Produced by V(D)J recombination, TCRs are often only recorded in the literature and data repositories as a combination of their V and J gene symbols, plus their hypervariable CDR3 amino acid sequence. However, numerous applications require full-length coding nucleotide sequences. Here we present Stitchr, a software tool developed to specifically address this limitation. Given minimal V/J/CDR3 information, Stitchr produces complete coding sequences representing a fully spliced TCR cDNA. Due to its modular design, Stitchr can be used for TCR engineering using either published germline or novel/modified variable and constant region sequences. Sequences produced by Stitchr were validated by synthesizing and transducing TCR sequences into Jurkat cells, recapitulating the expected antigen specificity of the parental TCR. Using a companion script, Thimble, we demonstrate that Stitchr can process a million TCRs in under ten minutes using a standard desktop personal computer. By systematizing the production and modification of TCR sequences, we propose that Stitchr will increase the speed, repeatability, and reproducibility of TCR research. Stitchr is available on GitHub.

TCR meta-clonotypes for biomarker discovery with tcrdist3 enabled identification of public, HLA-restricted clusters of SARS-CoV-2 TCRs

T-cell receptors (TCRs) encode clinically valuable information that reflects prior antigen exposure and potential future response. However, despite advances in deep repertoire sequencing, enormous TCR diversity complicates the use of TCR clonotypes as clinical biomarkers. We propose a new framework that leverages experimentally inferred antigen-associated TCRs to form meta-clonotypes - groups of biochemically similar TCRs - that can be used to robustly quantify functionally similar TCRs in bulk repertoires across individuals. We apply the framework to TCR data from COVID-19 patients, generating 1831 public TCR meta-clonotypes from the SARS-CoV-2 antigen-associated TCRs that have strong evidence of restriction to patients with a specific human leukocyte antigen (HLA) genotype. Applied to independent cohorts, meta-clonotypes targeting these specific epitopes were more frequently detected in bulk repertoires compared to exact amino acid matches, and 59.7% (1093/1831) were more abundant among COVID-19 patients that expressed the putative restricting HLA allele (false discovery rate [FDR]<0.01), demonstrating the potential utility of meta-clonotypes as antigen-specific features for biomarker development. To enable further applications, we developed an open-source software package, tcrdist3, that implements this framework and facilitates flexible workflows for distance-based TCR repertoire analysis.

T-cell receptor repertoires as potential diagnostic markers for patients with COVID-19

Objective

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an ongoing global health emergency. T-cell receptors (TCRs) are crucial mediators of antiviral adaptive immunity. This study sought to comprehensively characterize the TCR repertoire changes in patients with COVID-19.

Methods

A large sample size multi-center randomized controlled trial was implemented to study the features of the TCR repertoire and identify COVID-19 disease-related TCR sequences.

Results

It was found that some T-cell receptor beta chain (TCRβ) features differed markedly between COVID-19 patients and healthy controls, including decreased repertoire diversity, longer complementarity-determining region 3 (CDR3) length, skewed utilization of the TCRβ variable gene/joining gene (TRBV/J), and a high degree of TCRβ sharing in COVID-19 patients. Moreover, this analysis showed that TCR repertoire diversity declines with aging, which may be a cause of the higher infection and mortality rates in elderly patients. Importantly, a set of TCRβ clones that can distinguish COVID-19 patients from healthy controls with high accuracy was identified. Notably, this diagnostic model demonstrates 100% specificity and 82.68% sensitivity at 0–3 days post diagnosis.

Conclusions

This study lays the foundation for immunodiagnosis and the development of medicines and vaccines for COVID-19 patients.

Sleep restriction prior to antigen exposure does not alter the T cell receptor repertoire but impairs germinal center formation during a T cell-dependent B cell response in murine spleen

It is well known that sleep promotes immune functions. In line with this, a variety of studies in animal models and humans have shown that sleep restriction following an antigen challenge dampens the immune response on several levels which leads to e.g. worsening of disease outcome and reduction of vaccination efficiency, respectively. However, the inverse scenario with sleep restriction preceding an antigen challenge is only investigated in a few animal models where it has been shown to reduce antigen uptake and presentation as well as pathogen clearance and survival rates. Here, we use injection of sheep red blood cells to investigate the yet unknown effect on a T cell-dependent B cell response in a well-established mouse model. We found that 6 ​h of sleep restriction prior to the antigen challenge does not impact the T cell reaction including the T cell receptor repertoire but dampens the development of germinal centers which correlates with reduced antigen-specific antibody titer indicating an impaired B cell response. These changes concerned a functionally more relevant level than those found in the same experimental model with the inverse scenario when sleep restriction followed the antigen challenge. Taken together, our findings showed that the outcome of the T cell-dependent B cell response is indeed impacted by sleep restriction prior to the antigen challenge which highlights the clinical significance of this scenario and the need for further investigations in humans, for example concerning the effect of sleep restriction preceding a vaccination.

TCRβ rearrangements without a D segment are common, abundant, and public

The human body detects foreign pathogens by T cells with specific receptors. These are not directly encoded in the genome but generated in a random process that combines small gene segments into functional subunits of the receptor. The β-chain of the T cell receptor is normally composed of three such gene segments. Here we identify a group of T cells that lack the middle segment in their receptor sequence. We find that such sequences are mostly generated before birth, persist over a human lifetime, and, as a result, are excessively shared between individuals.

T cells play an important role in adaptive immunity. An enormous clonal diversity of T cells with a different specificity, encoded by the T cell receptor (TCR), protect the body against infection. Most TCRβ chains are generated from a V, D, and J segment during recombination in the thymus. Although complete absence of the D segment is not easily detectable from sequencing data, we find convincing evidence for a substantial proportion of TCRβ rearrangements lacking a D segment. Additionally, sequences without a D segment are more likely to be abundant within individuals and/or shared between individuals. Our analysis indicates that such sequences are preferentially generated during fetal development and persist within the elderly. Summarizing, TCRβ rearrangements without a D segment are not uncommon, and tend to allow for TCRβ chains with a high abundance in the naive repertoire.

Tools for fundamental analysis functions of TCR repertoires: a systematic comparison

The full set of T cell receptors (TCRs) in an individual is known as his or her TCR repertoire. Defining TCR repertoires under physiological conditions and in response to a disease or vaccine may lead to a better understanding of adaptive immunity and thus has great biological and clinical value. In the past decade, several high-throughput sequencing-based tools have been developed to assign TCRs to germline genes and to extract complementarity-determining region 3 (CDR3) sequences using different algorithms. Although these tools claim to be able to perform the full range of fundamental TCR repertoire analyses, there is no clear consensus of which tool is best suited to particular projects. Here, we present a systematic analysis of 12 available TCR repertoire analysis tools using simulated data, with an emphasis on fundamental analysis functions. Our results shed light on the detailed functions of TCR repertoire analysis tools and may therefore help researchers in the field to choose the right tools for their particular experimental design.

Quantifying T Cell Cross-Reactivity: Influenza and Coronaviruses

If viral strains are sufficiently similar in their immunodominant epitopes, then populations of cross-reactive T cells may be boosted by exposure to one strain and provide protection against infection by another at a later date. This type of pre-existing immunity may be important in the adaptive immune response to influenza and to coronaviruses. Patterns of recognition of epitopes by T cell clonotypes (a set of cells sharing the same T cell receptor) are represented as edges on a bipartite network. We describe different methods of constructing bipartite networks that exhibit cross-reactivity, and the dynamics of the T cell repertoire in conditions of homeostasis, infection and re-infection. Cross-reactivity may arise simply by chance, or because immunodominant epitopes of different strains are structurally similar. We introduce a circular space of epitopes, so that T cell cross-reactivity is a quantitative measure of the overlap between clonotypes that recognize similar (that is, close in epitope space) epitopes.

Mapping the spatial distribution of T cells in repertoire dimension

T cells mediate adaptive immunity in diverse anatomic compartments through recognition of specific antigens via unique T cell receptor (TCR) structures. However, little is known about the spatial distribution of an organism's TCR repertoire. Here, using high-throughput TCR sequencing (TCRseq), we investigated the TCR repertoires of sixteen tissues in healthy C57B/L6 mice. We found that TCR repertoires generally classified into three categories (lymph nodes, non-lymph node tissues and small intestine) based on sequence similarity. Clonal distribution and diversity analyses showed that small intestine compartment had a more skewed repertoire as compared to lymph nodes and non-lymph node tissues. However, analysis of TRBV and TRBJ gene usage across tissue compartments, as well as comparison of CDR3 length distributions, showed no significant tissue-dependent differences. Interestingly, analysis of clonotype sharing between mice showed that although non-redundant public clonotypes were found more easily in lymph nodes, small intestinal CD4 + T cells harbored more abundant public clonotypes. These findings under healthy physiological conditions offer an important reference dataset, which may contribute to our ability to better manipulate T cell responses against infection and vaccination.

High-throughput and single-cell T cell receptor sequencing technologies

T cells express T cell receptors (TCRs) composed of somatically recombined TCRα and TCRβ chains, which mediate recognition of major histocompatibility complex (MHC)-antigen complexes and drive the antigen-specific adaptive immune response to pathogens and cancer. The TCR repertoire in each individual is highly diverse, which allows for recognition of a wide array of foreign antigens, but also presents a challenge in analyzing this response using conventional methods. Recent studies have developed high-throughput sequencing technologies to identify TCR sequences, analyze their antigen specificities using experimental and computational tools, and pair TCRs with transcriptional and epigenetic cell state phenotypes in single cells. In this Review, we highlight these technological advances and describe how they have been applied to discover fundamental insights into T cell-mediated immunity.

3D genome organization during lymphocyte development and activation

Chromosomes have a complex three-dimensional (3D) architecture comprising A/B compartments, topologically associating domains and promoter-enhancer interactions. At all these levels, the 3D genome has functional consequences for gene transcription and therefore for cellular identity. The development and activation of lymphocytes involves strict control of gene expression by transcription factors (TFs) operating in a three-dimensionally organized chromatin landscape. As lymphocytes are indispensable for tissue homeostasis and pathogen defense, and aberrant lymphocyte activity is involved in a wide range of human morbidities, acquiring an in-depth understanding of the molecular mechanisms that control lymphocyte identity is highly relevant. Here we review current knowledge of the interplay between 3D genome organization and transcriptional control during B and T lymphocyte development and antigen-dependent activation, placing special emphasis on the role of TFs.

Trace Reconstruction Problems in Computational Biology

The problem of reconstructing a string from its error-prone copies, the trace reconstruction problem, was introduced by Vladimir Levenshtein two decades ago. While there has been considerable theoretical work on trace reconstruction, practical solutions have only recently started to emerge in the context of two rapidly developing research areas: immunogenomics and DNA data storage. In immunogenomics, traces correspond to mutated copies of genes, with mutations generated naturally by the adaptive immune system. In DNA data storage, traces correspond to noisy copies of DNA molecules that encode digital data, with errors being artifacts of the data retrieval process. In this paper, we introduce several new trace generation models and open questions relevant to trace reconstruction for immunogenomics and DNA data storage, survey theoretical results on trace reconstruction, and highlight their connections to computational biology. Throughout, we discuss the applicability and shortcomings of known solutions and suggest future research directions.