Can we predict T cell specificity with digital biology and machine learning?

Origins of T-cell-mediated autoimmunity in acquired aplastic anaemia

Acquired aplastic anaemia (AA) is an autoimmune bone marrow failure disease resulting from a cytotoxic T-cell-mediated attack on haematopoietic stem and progenitor cells (HSPCs). Despite significant progress in understanding the T-cell repertoire alterations in AA, identifying specific pathogenic T cells in AA patients has remained elusive, primarily due to the unknown antigenic targets of the autoimmune attack. In this review, we will synthesize findings from several decades of research to critically evaluate the current knowledge on T-cell repertoires in AA. We will highlight new insights gained from recent in vitro studies of candidate autoreactive T cells isolated from AA patients and will discuss efforts to identify shared T-cell clonotypes in AA. Finally, we will discuss emerging evidence on the potential T-cell cross-reactivity between HSPC and common viral epitopes that may contribute to the development of AA in some patients. We conclude by highlighting the areas of consensus and limitations, as well as the ongoing uncertainties, and we identify promising directions for future research in the field.

Tailoring nanovectors for optimal neoantigen vaccine efficacy

The primary objective of neoantigen vaccines is to elicit a robust anti-tumor immune response by generating neoantigen-specific T cells that can eradicate tumor cells. Despite substantial advancements in personalized neoantigen prediction using next-generation sequencing, machine learning, and mass spectrometry, challenges remain in efficiently expanding neoantigen-specific T cell populations in vivo. This challenge impedes the widespread clinical application of neoantigen vaccines. Nanovector-based neoantigen delivery systems have emerged as a promising solutions to this challenge. These nanovectors offer several advantages, such as enhanced stability, targeted intracellular delivery, sustained release, and improved antigen-presenting cell (APC) activation. Notably, they effectively deliver various neoantigen vaccine formulations (DC cell-based, synthetic long peptide (SLP)-based or DNA/mRNA-based) to APCs or T cells, thereby activating both CD4+ T and CD8+ T cells. This ultimately induces a specific anti-tumor immune response. This review focuses on recent innovations in neoantigen vaccine delivery vectors. We aim to identify optimal design parameters for vectors tailored to different neoantigen vaccine types, with an emphasis on enhancing the tumor microenvironment and stimulating the production of neoantigen-specific cytotoxic T cells. By maximizing the potential of these delivery systems, we aim to accelerate the clinical translation of neoantigen nanovaccines and advance cancer immunotherapy.

T-cell receptor structures and predictive models reveal comparable alpha and beta chain structural diversity despite differing genetic complexity

T-cell receptor (TCR) structures are currently under-utilised in early-stage drug discovery and repertoire-scale informatics. Here, we leverage a large dataset of solved TCR structures from Immunocore to evaluate the current state-of-the-art for TCR structure prediction, and identify which regions of the TCR remain challenging to model. Through clustering analyses and the training of a TCR-specific model capable of large-scale structure prediction, we find that the alpha chain VJ-recombined loop (CDR3α) is as structurally diverse and correspondingly difficult to predict as the beta chain VDJ-recombined loop (CDR3β). This differentiates TCR variable domain loops from the genetically analogous antibody loops and supports the conjecture that both TCR alpha and beta chains are deterministic of antigen specificity. We hypothesise that the larger number of alpha chain joining genes compared to beta chain joining genes compensates for the lack of a diversity gene segment. We also provide over 1.5M predicted TCR structures to enable repertoire structural analysis and elucidate strategies towards improving the accuracy of future TCR structure predictors. Our observations reinforce the importance of paired TCR sequence information and capture the current state-of-the-art for TCR structure prediction, while our model and 1.5M structure predictions enable the use of structural TCR information at an unprecedented scale.

GRATCR: Epitope-Specific T Cell Receptor Sequence Generation With Data-Efficient Pre-Trained Models

T cell receptors (TCRs) play a crucial role in numerous immunotherapies targeting tumor cells. However, their acquisition and optimization present significant challenges, involving laborious and time-consuming wet lab experimental resource. Deep generative models have demonstrated remarkable capabilities in functional protein sequence generation, offering a promising solution for enhancing the acquisition of specific TCR sequences. Here, we propose GRATCR, a framework incorporates two pre-trained modules through a novel "grafting" strategy, to de-novo generate TCR sequences targeting specific epitopes. Experimental results demonstrate that TCRs generated by GRATCR exhibit higher specificity toward desired epitopes and are more biologically functional compared with the state-of-the-art model, by using significantly fewer training data. Additionally, the generated sequences display novelty compared to natural sequences, and the interpretability evaluation further confirmed that the model is capable of capturing important binding patterns.

Leveraging artificial intelligence and machine learning to accelerate discovery of disease-modifying therapies in type 1 diabetes

Progress in developing therapies for the maintenance of endogenous insulin secretion in, or the prevention of, type 1 diabetes has been hindered by limited animal models, the length and cost of clinical trials, difficulties in identifying individuals who will progress faster to a clinical diagnosis of type 1 diabetes, and heterogeneous clinical responses in intervention trials. Classic placebo-controlled intervention trials often include monotherapies, broad participant populations and extended follow-up periods focused on clinical endpoints. While this approach remains the 'gold standard' of clinical research, efforts are underway to implement new approaches harnessing the power of artificial intelligence and machine learning to accelerate drug discovery and efficacy testing. Here, we review emerging approaches for repurposing agents used to treat diseases that share pathogenic pathways with type 1 diabetes and selecting synergistic combinations of drugs to maximise therapeutic efficacy. We discuss how emerging multi-omics technologies, including analysis of antigen processing and presentation to adaptive immune cells, may lead to the discovery of novel biomarkers and subsequent translation into antigen-specific immunotherapies. We also discuss the potential for using artificial intelligence to create 'digital twin' models that enable rapid in silico testing of personalised agents as well as dose determination. To conclude, we discuss some limitations of artificial intelligence and machine learning, including issues pertaining to model interpretability and bias, as well as the continued need for validation studies via confirmatory intervention trials.

Comprehensive epitope mutational scan database enables accurate T cell receptor cross-reactivity prediction

Predicting T cell receptor (TCR) activation is challenging due to the lack of both unbiased benchmarking datasets and computational methods that are sensitive to small mutations to a peptide. To address these challenges, we curated a comprehensive database, called BATCAVE, encompassing complete single amino acid mutational assays of more than 22,000 TCR-peptide pairs, centered around 25 immunogenic human and mouse epitopes, across both major histocompatibility complex classes, against 151 TCRs. We then present an interpretable Bayesian model, called BATMAN, that can predict the set of peptides that activates a TCR. We also developed an active learning version of BATMAN, which can efficiently learn the binding profile of a novel TCR by selecting an informative yet small number of peptides to assay. When validated on our database, BATMAN outperforms existing methods and reveals important biochemical predictors of TCR-peptide interactions. Finally, we demonstrate the broad applicability of BATMAN, including for predicting off-target effects for TCR-based therapies and polyclonal T cell responses.

Exploring the role of T cells in Alzheimer's and other neurodegenerative diseases: Emerging therapeutic insights from the T Cells in the Brain symposium

This proceedings article summarizes the inaugural "T Cells in the Brain" symposium held at Columbia University. Experts gathered to explore the role of T cells in neurodegenerative diseases. Key topics included characterization of antigen-specific immune responses, T cell receptor (TCR) repertoire, microbial etiology in Alzheimer's disease (AD), and microglia-T cell crosstalk, with a focus on how T cells affect neuroinflammation and AD biomarkers like amyloid beta and tau. The symposium also examined immunotherapies for AD, including the Valacyclovir Treatment of Alzheimer's Disease (VALAD) trial, and two clinical trials leveraging regulatory T cell approaches for multiple sclerosis and amyotrophic lateral sclerosis therapy. Additionally, single-cell RNA/TCR sequencing of T cells and other immune cells provided insights into immune dynamics in neurodegenerative diseases. This article highlights key findings from the symposium and outlines future research directions to further understand the role of T cells in neurodegeneration, offering innovative therapeutic approaches for AD and other neurodegenerative diseases. HIGHLIGHTS: Researchers gathered to discuss approaches to study T cells in brain disorders. New technologies allow high-throughput screening of antigen-specific T cells. Microbial infections can precede several serious and chronic neurological diseases. Central and peripheral T cell responses shape neurological disease pathology. Immunotherapy can induce regulatory T cell responses in neuroinflammatory disorders.

Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning

Machine learning (ML) has shown great potential in the adaptive immune receptor repertoire (AIRR) field. However, there is a lack of large-scale ground-truth experimental AIRR data suitable for AIRR-ML-based disease diagnostics and therapeutics discovery. Simulated ground-truth AIRR data are required to complement the development and benchmarking of robust and interpretable AIRR-ML methods where experimental data is currently inaccessible or insufficient. The challenge for simulated data to be useful is incorporating key features observed in experimental repertoires. These features, such as antigen or disease-associated immune information, cause AIRR-ML problems to be challenging. Here, we introduce LIgO, a software suite, which simulates AIRR data for the development and benchmarking of AIRR-ML methods. LIgO incorporates different types of immune information both on the receptor and the repertoire level and preserves native-like generation probability distribution. Additionally, LIgO assists users in determining the computational feasibility of their simulations. We show two examples where LIgO supports the development and validation of AIRR-ML methods: (i) how individuals carrying out-of-distribution immune information impacts receptor-level prediction performance and (ii) how immune information co-occurring in the same AIRs impacts the performance of conventional receptor-level encoding and repertoire-level classification approaches. LIgO guides the advancement and assessment of interpretable AIRR-ML methods.

Feature selection enhances peptide binding predictions for TCR-specific interactions

Introduction

T-cell receptors (TCRs) play a critical role in the immune response by recognizing specific ligand peptides presented by major histocompatibility complex (MHC) molecules. Accurate prediction of peptide binding to TCRs is essential for advancing immunotherapy, vaccine design, and understanding mechanisms of autoimmune disorders.

Methods

This study presents a theoretical approach that explores the impact of feature selection techniques on enhancing the predictive accuracy of peptide binding models tailored for specific TCRs. To evaluate our approach across different TCR systems, we utilized a dataset that includes peptide libraries tested against three distinct murine TCRs. A broad range of physicochemical properties, including amino acid composition, dipeptide composition, and tripeptide features, were integrated into the machine learning-based feature selection framework to identify key properties contributing to binding affinity.

Results

Our analysis reveals that leveraging optimized feature subsets not only simplifies the model complexity but also enhances predictive performance, enabling more precise identification of TCR peptide interactions. The results of our feature selection method are consistent with findings from hybrid approaches that utilize both sequence and structural data as input as well as experimental data.

Discussion

Our theoretical approach highlights the role of feature selection in peptide-TCR interactions, providing a quantitative tool for uncovering the molecular mechanisms of the T-cell response and assisting in the design of more advanced targeted therapeutics.

Contrastive learning of T cell receptor representations

Computational prediction of the interaction of T cell receptors (TCRs) and their ligands is a grand challenge in immunology. Despite advances in high-throughput assays, specificity-labeled TCR data remain sparse. In other domains, the pre-training of language models on unlabeled data has been successfully used to address data bottlenecks. However, it is unclear how to best pre-train protein language models for TCR specificity prediction. Here, we introduce a TCR language model called SCEPTR (simple contrastive embedding of the primary sequence of T cell receptors), which is capable of data-efficient transfer learning. Through our model, we introduce a pre-training strategy combining autocontrastive learning and masked-language modeling, which enables SCEPTR to achieve its state-of-the-art performance. In contrast, existing protein language models and a variant of SCEPTR pre-trained without autocontrastive learning are outperformed by sequence alignment-based methods. We anticipate that contrastive learning will be a useful paradigm to decode the rules of TCR specificity. A record of this paper's transparent peer review process is included in the supplemental information.

TCR3d 2.0: expanding the T cell receptor structure database with new structures, tools and interactions

Recognition of antigens by T cell receptors (TCRs) is a key component of adaptive immunity. Understanding the structures of these TCR interactions provides major insights into immune protection and diseases, and enables design of therapeutics, vaccines and predictive modeling algorithms. Previously, we released TCR3d, a database and resource for structures of TCRs and their recognition. Due to the growth of available structures and categories of complexes, the content of TCR3d has expanded substantially in the past 5 years. This expansion includes new tables dedicated to TCR mimic antibody complex structures, TCR-CD3 complexes and annotated Class I and II peptide-MHC complexes. Additionally, tools are available for users to calculate docking geometries for input TCR and TCR mimic complex structures. The core tables of TCR-peptide-MHC complexes have grown by 50%, and include binding affinity data for experimentally determined structures. These major content and feature updates enhance TCR3d as a resource for immunology, therapeutics and structural biology research, and enable advanced approaches for predictive TCR modeling and design. TCR3d is available at: https://tcr3d.ibbr.umd.edu.

High-throughput and multimodal profiling of antigen-specific T cells with a droplet-based cell-cell interaction screening platform

Identifying antigen-specific T cells from tumor-infiltrating lymphocytes is essential for designing effective T cell immunotherapies. Traditional methods can detect antigen-specific T cells but struggle with high-throughput screening and multimodal profiling simultaneously. To address this issue, we developed DropCCI, a new strategy that transfers antigen information to co-incubated T cells for high-throughput, non-contaminated multimodal profiling. In DropCCI, droplets encapsulated DNA barcodes and antigen-loaded antigen-presenting cells (APCs), while click chemistry-modified T cells were injected into these droplets to capture free barcodes and acquire the corresponding antigen information. Following cell-cell interaction, APCs were removed via streptavidin-biotin conjugation, to prevent contamination. The resulting T cells underwent single-cell omics sequencing for comprehensive profiling of their antigen specificity, transcriptome, and genomics accurately. This click-chemistry method allowed detection of antigen-specific T cells without lysing APCs, avoiding cross-cell contamination and enabling low-noise multimodal profiling of primary T cells. With a completion time within 12 h and no requirement for complex equipment, DropCCI provides unbiased single-cell sequencing results that offer a comprehensive understanding of anti-tumor T cell responses. The concept of DropCCI holds great promise not only for advancing the field of T cell immunotherapy but also for its potential application in studying other cell-cell interactions.

The Type 1 Diabetes T Cell Receptor and B Cell Receptor Repository in the AIRR Data Commons: a practical guide for access, use and contributions through the Type 1 Diabetes AIRR Consortium

Human molecular genetics has brought incredible insights into the variants that confer risk for the development of tissue-specific autoimmune diseases, including type 1 diabetes. The hallmark cell-mediated immune destruction that is characteristic of type 1 diabetes is closely linked with risk conferred by the HLA class II gene locus, in combination with a broad array of additional candidate genes influencing islet-resident beta cells within the pancreas, as well as function, phenotype and trafficking of immune cells to tissues. In addition to the well-studied germline SNP variants, there are critical contributions conferred by T cell receptor (TCR) and B cell receptor (BCR) genes that undergo somatic recombination to yield the Adaptive Immune Receptor Repertoire (AIRR) responsible for autoimmunity in type 1 diabetes. We therefore created the T1D TCR/BCR Repository (The Type 1 Diabetes T Cell Receptor and B Cell Receptor Repository) to study these highly variable and dynamic gene rearrangements. In addition to processed TCR and BCR sequences, the T1D TCR/BCR Repository includes detailed metadata (e.g. participant demographics, disease-associated parameters and tissue type). We introduce the Type 1 Diabetes AIRR Consortium goals and outline methods to use and deposit data to this comprehensive repository. Our ultimate goal is to facilitate research community access to rich, carefully annotated immune AIRR datasets to enable new scientific inquiry and insight into the natural history and pathogenesis of type 1 diabetes.

Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning

The adaptive immune system holds invaluable information on past and present immune responses in the form of B and T cell receptor sequences, but we are limited in our ability to decode this information. Machine learning approaches are under active investigation for a range of tasks relevant to understanding and manipulating the adaptive immune receptor repertoire, including matching receptors to the antigens they bind, generating antibodies or T cell receptors for use as therapeutics, and diagnosing disease based on patient repertoires. Progress on these tasks has the potential to substantially improve the development of vaccines, therapeutics, and diagnostics, as well as advance our understanding of fundamental immunological principles. We outline key challenges for the field, highlighting the need for software benchmarking, targeted large-scale data generation, and coordinated research efforts.

Multiple instance learning method based on convolutional neural network and self-attention for early cancer detection

Early cancer detection using T-cell receptor sequencing (TCR-seq) and multiple instances learning methods has shown significant effectiveness. We introduce a multiple instance learning method based on convolutional neural networks and self-attention (MICA). First, MICA preprocesses TCR-seq using word vectors and then extracts features using convolutional neural networks. Second, MICA uses an enhanced self-attention mechanism to extract relational features of instances. Finally, MICA can extract the crucial TCR-seq. After cross-validation, MICA achieves an area under the curve (AUC) of 0.911 and 0.946 on the lung and thyroid cancer datasets, which are 7.1% and 2.1% higher than other methods, respectively.

Quantifying conformational changes in the TCR:pMHC-I binding interface

Background

T cells form one of the key pillars of adaptive immunity. Using their surface bound T cell antigen receptors (TCRs), these cells screen millions of antigens presented by major histocompatibility complex (MHC) or MHC-like molecules. In other protein families, the dynamics of protein-protein interactions have important implications for protein function. Case studies of TCR:class I peptide-MHCs (pMHC-Is) structures have reported mixed results on whether the binding interfaces undergo conformational change during engagement and no robust statistical quantification has been done to generalise these results. Thus, it remains an open question of whether movement occurs in the binding interface that enables the recognition and activation of T cells.

Methods

In this work, we quantify the conformational changes in the TCR:pMHC-I binding interface by creating a dataset of 391 structures, comprising 22 TCRs, 19 MHC alleles, and 79 peptide structures in both unbound (apo) and bound (holo) conformations.

Results

In support of some case studies, we demonstrate that all complementarity determining region (CDR) loops move to a certain extent but only CDR3α and CDR3β loops modify their shape when binding pMHC-Is. We also map the contacts between TCRs and pMHC-Is, generating a novel fingerprint of TCRs on MHC molecules and show that the CDR3α tends to bind the N-terminus of the peptide and the CDR3β tends to bind the C-terminus of the peptide. Finally, we show that the presented peptides can undergo conformational changes when engaged by TCRs, as has been reported in past literature, but novelly show these changes depend on how the peptides are anchored in the MHC binding groove.

Conclusions

Our work has implications in understanding the behaviour of TCR:pMHC-I interactions and providing insights that can be used for modelling Tcell antigen specificity, an ongoing grand challenge in immunology.

Vaccine development using artificial intelligence and machine learning: A review

The COVID-19 pandemic has underscored the critical importance of effective vaccines, yet their development is a challenging and demanding process. It requires identifying antigens that elicit protective immunity, selecting adjuvants that enhance immunogenicity, and designing delivery systems that ensure optimal efficacy. Artificial intelligence (AI) can facilitate this process by using machine learning methods to analyze large and diverse datasets, suggest novel vaccine candidates, and refine their design and predict their performance. This review explores how AI can be applied to various aspects of vaccine development, such as predicting immune response from protein sequences, discovering adjuvants, optimizing vaccine doses, modeling vaccine supply chains, and predicting protein structures. We also address the challenges and ethical issues that emerge from the use of AI in vaccine development, such as data privacy, algorithmic bias, and health data sensitivity. We contend that AI has immense potential to accelerate vaccine development and respond to future pandemics, but it also requires careful attention to the quality and validity of the data and methods used.

A comprehensive benchmarking for evaluating TCR embeddings in modeling TCR-epitope interactions

The complexity of T cell receptor (TCR) sequences, particularly within the complementarity-determining region 3 (CDR3), requires efficient embedding methods for applying machine learning to immunology. While various TCR CDR3 embedding strategies have been proposed, the absence of their systematic evaluations created perplexity in the community. Here, we extracted CDR3 embedding models from 19 existing methods and benchmarked these models with four curated datasets by accessing their impact on the performance of TCR downstream tasks, including TCR-epitope binding affinity prediction, epitope-specific TCR identification, TCR clustering, and visualization analysis. We assessed these models utilizing eight downstream classifiers and five downstream clustering methods, with the performance measured by a diverse range of metrics for precision, robustness, and usability. Overall, handcrafted embeddings outperformed data-driven ones in modeling TCR-epitope interactions. To further refine our comparative findings, we developed an all-in-one TCR CDR3 embedding package comprising all evaluated embedding models. This package will assist users in easily selecting suitable embedding models for their data.

Chemical-Specific T Cell Tests Aim to Bridge a Gap in Skin Sensitization Evaluation

T cell activation is the final key event (KE4) in the adverse outcome pathway (AOP) of skin sensitization. However, validated new approach methodologies (NAMs) for evaluating this step are missing. Accordingly, chemicals that activate an unusually high frequency of T cells, as does the most prevalent metal allergen nickel, are not yet identified in a regulatory context. T cell reactivity to chemical sensitizers might be especially relevant in real-life scenarios, where skin injury, co-exposure to irritants in chemical mixtures, or infections may trigger the heterologous innate immune stimulation necessary to induce adaptive T cell responses. Additionally, cross-reactivity, which underlies cross-allergies, can only be assessed by T cell tests. To date, several experimental T cell tests are available that use primary naïve and memory CD4+ and CD8+ T cells from human blood. These include priming and lymphocyte proliferation tests and, most recently, activation-induced marker (AIM) assays. All approaches are challenged by chemical-mediated toxicity, inefficient or unknown generation of T cell epitopes, and a low throughput. Here, we summarize solutions and strategies to confirm in vitro T cell signals. Broader application and standardization are necessary to possibly define chemical applicability domains and to strengthen the role of T cell tests in regulatory risk assessment.

XCMS-METLIN: data-driven metabolite, lipid, and chemical analysis

In this Correspondence, G. Siuzdak and colleagues present XCMS-METLIN, an extensive resource for metabolomics, lipidomics, and chemical analysis.

TIRTL-seq: Deep, quantitative, and affordable paired TCR repertoire sequencing

ɑ/β T cells are key players in adaptive immunity. The specificity of T cells is determined by the sequences of the hypervariable T cell receptor (TCR) ɑ and β chains. Although bulk TCR sequencing offers a cost-effective approach for in-depth TCR repertoire profiling, it does not provide chain pairings, which are essential for determining T cell specificity. In contrast, single-cell TCR sequencing technologies produce paired chain data, but are limited in throughput to thousands of cells and are cost-prohibitive for cohort-scale studies. Here, we present TIRTL-seq (Throughput-Intensive Rapid TCR Library sequencing), a novel approach that generates ready-to-sequence TCR libraries from live cells in less than 7 hours. The protocol is optimized for use with non-contact liquid handlers in an automation-friendly 384-well plate format. Reaction volume miniaturization reduces library preparation costs to <$0.50 per well. The core principle of TIRTL-seq is the parallel generation of hundreds of libraries providing multiple biological replicates from a single sample that allows precise inference of both frequencies of individual clones and TCR chain pairings from well-occurrence patterns. We demonstrate scalability of our approach up to 1 million unique paired αβTCR clonotypes corresponding to over 30 million T cells per sample at a cost of less than $2000. For a sample of 10 million cells the cost is ~$200. We benchmarked TIRTL-seq against state-of-the-art 5'RACE bulk TCR-seq and 10x Genomics Chromium technologies on longitudinal samples. We show that TIRTL-seq is able to quantitatively identify expanding and contracting clonotypes between timepoints while providing accurate TCR chain pairings, including distinct temporal dynamics of SARS-CoV-2-specific and EBV-specific CD8+ T cell responses after infection. While clonal expansion was followed by sharp contraction for SARS-CoV-2 specific TCRs, EBV-specific TCRs remained stable once established. The sequences of both ɑ and β TCR chains are essential for determining T cell specificity. As the field moves towards greater applications in diagnostics and immunotherapy that rely on TCR specificity, we anticipate that our scalable paired TCR sequencing methodology will be instrumental for collecting large paired-chain datasets and ultimately extracting therapeutically relevant information from the TCR repertoire.

Limits on inferring T cell specificity from partial information

A key challenge in molecular biology is to decipher the mapping of protein sequence to function. To perform this mapping requires the identification of sequence features most informative about function. Here, we quantify the amount of information (in bits) that T cell receptor (TCR) sequence features provide about antigen specificity. We identify informative features by their degree of conservation among antigen-specific receptors relative to null expectations. We find that TCR specificity synergistically depends on the hypervariable regions of both receptor chains, with a degree of synergy that strongly depends on the ligand. Using a coincidence-based approach to measuring information enables us to directly bound the accuracy with which TCR specificity can be predicted from partial matches to reference sequences. We anticipate that our statistical framework will be of use for developing machine learning models for TCR specificity prediction and for optimizing TCRs for cell therapies. The proposed coincidence-based information measures might find further applications in bounding the performance of pairwise classifiers in other fields.

Overcoming immune evasion from post-translational modification of a mutant KRAS epitope to achieve TCR-T cell-mediated antitumor activity

T cell receptor (TCR)-T cell immunotherapy, in which T cells are engineered to express a TCR targeting a tumor epitope, is a form of adoptive cell therapy (ACT) that has exhibited promise against various tumor types. Mutants of oncoprotein KRAS, particularly at glycine-12 (G12), are frequent drivers of tumorigenicity, making them attractive targets for TCR-T cell therapy. However, class I-restricted TCRs specifically targeting G12-mutant KRAS epitopes in the context of tumors expressing HLA-A2, the most common human HLA-A allele, have remained elusive despite evidence an epitope encompassing such mutations can bind HLA-A2 and induce T cell responses. We report post-translational modifications (PTMs) on this epitope may allow tumor cells to evade immunologic pressure from TCR-T cells. A lysine side chain-methylated KRAS G12V peptide, rather than the unmodified epitope, may be presented in HLA-A2 by tumor cells and impact TCR recognition. Using a novel computationally guided approach, we developed by mutagenesis TCRs that recognize this methylated peptide, enhancing tumor recognition and destruction. Additionally, we identified TCRs with similar functional activity in normal repertoires from primary T cells by stimulation with modified peptide, clonal expansion, and selection. Mechanistically, a gene knockout screen to identify mechanism(s) by which tumor cells methylate/demethylate this epitope unveiled SPT6 as a demethylating protein that could be targeted to improve effectiveness of these new TCRs. Our findings highlight the role of PTMs in immune evasion and suggest identifying and targeting such modifications should make effective ACTs available for a substantially greater range of tumors than the current therapeutic landscape.

One-sentence summary

Tumor cell methylation of KRAS G12V epitope in HLA-A2 permits immune evasion, and new TCRs were generated to overcome this with engineered cell therapy.

Feature Selection Enhances Peptide Binding Predictions for TCR-Specific Interactions

T-cell receptors (TCRs) play a critical role in the immune response by recognizing specific ligand peptides presented by major histocompatibility complex (MHC) molecules. Accurate prediction of peptide binding to TCRs is essential for advancing immunotherapy, vaccine design, and understanding mechanisms of autoimmune disorders. This study presents a novel theoretical method that explores the impact of feature selection techniques on enhancing the predictive accuracy of peptide binding models tailored for specific TCRs. To evaluate the universality of our approach across different TCR systems, we utilized a dataset that includes peptide libraries tested against three distinct murine TCRs. A broad range of physicochemical properties, including amino acid composition, dipeptide composition, and tripeptide features, were integrated into the machine learning-based feature selection framework to identify key features contributing to binding affinity. Our analysis reveals that leveraging optimized feature subsets not only simplifies the model complexity but also enhances predictive performance, enabling more precise identification of TCR-peptide interactions. The results of our feature selection method are consistent with findings from hybrid approaches that utilize both sequence and structural data as input as well as experimental data. Our theoretical approach highlights the role of feature selection in peptide-TCR interactions, providing a powerful tool for uncovering the molecular mechanisms of the T-cell response and assisting in the design of more advanced targeted therapeutics.

Predicting Antigen-Specificities of Orphan T Cell Receptors from Cancer Patients with TCRpcDist

Approaches to analyze and cluster T-cell receptor (TCR) repertoires to reflect antigen specificity are critical for the diagnosis and prognosis of immune-related diseases and the development of personalized therapies. Sequence-based approaches showed success but remain restrictive, especially when the amount of experimental data used for the training is scarce. Structure-based approaches which represent powerful alternatives, notably to optimize TCRs affinity toward specific epitopes, show limitations for large-scale predictions. To handle these challenges, TCRpcDist is presented, a 3D-based approach that calculates similarities between TCRs using a metric related to the physico-chemical properties of the loop residues predicted to interact with the epitope. By exploiting private and public datasets and comparing TCRpcDist with competing approaches, it is demonstrated that TCRpcDist can accurately identify groups of TCRs that are likely to bind the same epitopes. Importantly, the ability of TCRpcDist is experimentally validated to determine antigen specificities (neoantigens and tumor-associated antigens) of orphan tumor-infiltrating lymphocytes (TILs) in cancer patients. TCRpcDist is thus a promising approach to support TCR repertoire analysis and TCR deorphanization for individualized treatments including cancer immunotherapies.

Yeast-based screening platforms to understand and improve human health

Detailed molecular understanding of the human organism is essential to develop effective therapies. Saccharomyces cerevisiae has been used extensively for acquiring insights into important aspects of human health, such as studying genetics and cell-cell communication, elucidating protein-protein interaction (PPI) networks, and investigating human G protein-coupled receptor (hGPCR) signaling. We highlight recent advances and opportunities of yeast-based technologies for cost-efficient chemical library screening on hGPCRs, accelerated deciphering of PPI networks with mating-based screening and selection, and accurate cell-cell communication with human immune cells. Overall, yeast-based technologies constitute an important platform to support basic understanding and innovative applications towards improving human health.

The Observed T Cell Receptor Space database enables paired-chain repertoire mining, coherence analysis, and language modeling

T cell activation is governed through T cell receptors (TCRs), heterodimers of two sequence-variable chains (often an α and β chain) that synergistically recognize antigen fragments presented on cell surfaces. Despite this, there only exist repositories dedicated to collecting single-chain, not paired-chain, TCR sequence data. We addressed this gap by creating the Observed TCR Space (OTS) database, a source of consistently processed and annotated, full-length, paired-chain TCR sequences. Currently, OTS contains 5.35 million redundant (1.63 million non-redundant), predominantly human sequences from across 50 studies and at least 75 individuals. Using OTS, we identify pairing biases, public TCRs, and distinct chain coherence patterns relative to antibodies. We also release a paired-chain TCR language model, providing paired embedding representations and a method for residue in-filling conditional on the partner chain. OTS will be updated as a central community resource and is freely downloadable and available as a web application.

Predicting T cell receptor functionality against mutant epitopes

Cancer cells and pathogens can evade T cell receptors (TCRs) via mutations in immunogenic epitopes. TCR cross-reactivity (i.e., recognition of multiple epitopes with sequence similarities) can counteract such escape but may cause severe side effects in cell-based immunotherapies through targeting self-antigens. To predict the effect of epitope point mutations on T cell functionality, we here present the random forest-based model Predicting T Cell Epitope-Specific Activation against Mutant Versions (P-TEAM). P-TEAM was trained and tested on three datasets with TCR responses to single-amino-acid mutations of the model epitope SIINFEKL, the tumor neo-epitope VPSVWRSSL, and the human cytomegalovirus antigen NLVPMVATV, totaling 9,690 unique TCR-epitope interactions. P-TEAM was able to accurately classify T cell reactivities and quantitatively predict T cell functionalities for unobserved single-point mutations and unseen TCRs. Overall, P-TEAM provides an effective computational tool to study T cell responses against mutated epitopes.

Identification and structural characterization of a mutant KRAS-G12V specific TCR restricted by HLA-A3

Mutations in KRAS are some of the most common across multiple cancer types and are thus attractive targets for therapy. Recent studies demonstrated that mutant KRAS generates immunogenic neoantigens that are targetable by adoptive T-cell therapy in metastatic diseases. To expand mutant KRAS-specific immunotherapies, it is critical to identify additional HLA-I allotypes that can present KRAS neoantigens and their cognate T-cell receptors (TCR). Here, we identified a murine TCR specific to a KRAS-G12V neoantigen (7VVVGAVGVGK16) using a vaccination approach with transgenic mice expressing HLA-A*03:01 (HLA-A3). This TCR demonstrated exquisite specificity for mutant G12V and not WT KRAS peptides. To investigate the molecular basis for neoantigen recognition by this TCR, we determined its structure in complex with HLA-A3(G12V). G12V-TCR CDR3β and CDR1β formed a hydrophobic pocket to interact with p6 Val of the G12V but not the WT KRAS peptide. To improve the tumor sensitivity of this TCR, we designed rational substitutions to improve TCR:HLA-A3 contacts. Two substitutions exhibited modest improvements in TCR binding avidity to HLA-A3 (G12V) but did not sufficiently improve T-cell sensitivity for further clinical development. Our study provides mechanistic insight into how TCRs detect neoantigens and reveals the challenges in targeting KRAS-G12V mutations.

TCR-H: explainable machine learning prediction of T-cell receptor epitope binding on unseen datasets

Artificial-intelligence and machine-learning (AI/ML) approaches to predicting T-cell receptor (TCR)-epitope specificity achieve high performance metrics on test datasets which include sequences that are also part of the training set but fail to generalize to test sets consisting of epitopes and TCRs that are absent from the training set, i.e., are 'unseen' during training of the ML model. We present TCR-H, a supervised classification Support Vector Machines model using physicochemical features trained on the largest dataset available to date using only experimentally validated non-binders as negative datapoints. TCR-H exhibits an area under the curve of the receiver-operator characteristic (AUC of ROC) of 0.87 for epitope 'hard splitting' (i.e., on test sets with all epitopes unseen during ML training), 0.92 for TCR hard splitting and 0.89 for 'strict splitting' in which neither the epitopes nor the TCRs in the test set are seen in the training data. Furthermore, we employ the SHAP (Shapley additive explanations) eXplainable AI (XAI) method for post hoc interrogation to interpret the models trained with different hard splits, shedding light on the key physiochemical features driving model predictions. TCR-H thus represents a significant step towards general applicability and explainability of epitope:TCR specificity prediction.

TSpred: a robust prediction framework for TCR-epitope interactions using paired chain TCR sequence data

MOTIVATION

Prediction of T-cell receptor (TCR)-epitope interactions is important for many applications in biomedical research, such as cancer immunotherapy and vaccine design. The prediction of TCR-epitope interactions remains challenging especially for novel epitopes, due to the scarcity of available data.

RESULTS

We propose TSpred, a new deep learning approach for the pan-specific prediction of TCR binding specificity based on paired chain TCR data. We develop a robust model that generalizes well to unseen epitopes by combining the predictive power of CNN and the attention mechanism. In particular, we design a reciprocal attention mechanism which focuses on extracting the patterns underlying TCR-epitope interactions. Upon a comprehensive evaluation of our model, we find that TSpred achieves state-of-the-art performances in both seen and unseen epitope specificity prediction tasks. Also, compared to other predictors, TSpred is more robust to bias related to peptide imbalance in the dataset. In addition, the reciprocal attention component of our model allows for model interpretability by capturing structurally important binding regions. Results indicate that TSpred is a robust and reliable method for the task of TCR-epitope binding prediction.

AVAILABILITY AND IMPLEMENTATION

Source code is available at https://github.com/ha01994/TSpred.

Harmonizing immune cell sequences for computational analysis with large language models

We present SEQuence Weighted Alignment for Sorting and Harmonization (Seqwash), an algorithm designed to process sequencing profiles utilizing large language models. Seqwash harmonizes immune cell sequences into a unified representation, empowering LLMs to embed meaningful patterns while eliminating irrelevant information. Evaluations using immune cell sequencing data showcase Seqwash's efficacy in standardizing profiles, leading to improved feature quality and enhanced performance in both supervised and unsupervised downstream tasks for sequencing data.

TCR clustering by contrastive learning on antigen specificity

Effective clustering of T-cell receptor (TCR) sequences could be used to predict their antigen-specificities. TCRs with highly dissimilar sequences can bind to the same antigen, thus making their clustering into a common antigen group a central challenge. Here, we develop TouCAN, a method that relies on contrastive learning and pretrained protein language models to perform TCR sequence clustering and antigen-specificity predictions. Following training, TouCAN demonstrates the ability to cluster highly dissimilar TCRs into common antigen groups. Additionally, TouCAN demonstrates TCR clustering performance and antigen-specificity predictions comparable to other leading methods in the field.

Structure-based prediction of T cell receptor recognition of unseen epitopes using TCRen

T cell receptor (TCR) recognition of foreign peptides presented by major histocompatibility complex protein is a major event in triggering the adaptive immune response to pathogens or cancer. The prediction of TCR-peptide interactions has great importance for therapy of cancer as well as infectious and autoimmune diseases but remains a major challenge, particularly for novel (unseen) peptide epitopes. Here we present TCRen, a structure-based method for ranking candidate unseen epitopes for a given TCR. The first stage of the TCRen pipeline is modeling of the TCR-peptide-major histocompatibility complex structure. Then a TCR-peptide residue contact map is extracted from this structure and used to rank all candidate epitopes on the basis of an interaction score with the target TCR. Scoring is performed using an energy potential derived from the statistics of TCR-peptide contact preferences in existing crystal structures. We show that TCRen has high performance in discriminating cognate versus unrelated peptides and can facilitate the identification of cancer neoepitopes recognized by tumor-infiltrating lymphocytes.

HeteroTCR: A heterogeneous graph neural network-based method for predicting peptide-TCR interaction

Identifying interactions between T-cell receptors (TCRs) and immunogenic peptides holds profound implications across diverse research domains and clinical scenarios. Unsupervised clustering models (UCMs) cannot predict peptide-TCR binding directly, while supervised predictive models (SPMs) often face challenges in identifying antigens previously unencountered by the immune system or possessing limited TCR binding repertoires. Therefore, we propose HeteroTCR, an SPM based on Heterogeneous Graph Neural Network (GNN), to accurately predict peptide-TCR binding probabilities. HeteroTCR captures within-type (TCR-TCR or peptide-peptide) similarity information and between-type (peptide-TCR) interaction insights for predictions on unseen peptides and TCRs, surpassing limitations of existing SPMs. Our evaluation shows HeteroTCR outperforms state-of-the-art models on independent datasets. Ablation studies and visual interpretation underscore the Heterogeneous GNN module's critical role in enhancing HeteroTCR's performance by capturing pivotal binding process features. We further demonstrate the robustness and reliability of HeteroTCR through validation using single-cell datasets, aligning with the expectation that pMHC-TCR complexes with higher predicted binding probabilities correspond to increased binding fractions.

Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy

Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.

VitTCR: A deep learning method for peptide recognition prediction

This study introduces VitTCR, a predictive model based on the vision transformer (ViT) architecture, aimed at identifying interactions between T cell receptors (TCRs) and peptides, crucial for developing cancer immunotherapies and vaccines. VitTCR converts TCR-peptide interactions into numerical AtchleyMaps using Atchley factors for prediction, achieving AUROC (0.6485) and AUPR (0.6295) values. Benchmark analysis indicates VitTCR's performance is comparable to other models, with further comparative studies suggested to understand its effectiveness in varied contexts. Additionally, integrating a positional bias weight matrix (PBWM), derived from amino acid contact probabilities in structurally resolved pMHC-TCR complexes, slightly improves VitTCR's accuracy. The model's predictions show weak yet statistically significant correlations with immunological factors like T cell clonal expansion and activation percentages, underscoring the biological relevance of VitTCR's predictive capabilities. VitTCR emerges as a valuable computational tool for predicting TCR-peptide interactions, offering insights for immunotherapy and vaccine development.

Unified cross-modality integration and analysis of T cell receptors and T cell transcriptomes by low-resource-aware representation learning

Single-cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) are pivotal for investigating T cell heterogeneity. Integrating these modalities, which is expected to uncover profound insights in immunology that might otherwise go unnoticed with a single modality, faces computational challenges due to the low-resource characteristics of the multimodal data. Herein, we present UniTCR, a novel low-resource-aware multimodal representation learning framework designed for the unified cross-modality integration, enabling comprehensive T cell analysis. By designing a dual-modality contrastive learning module and a single-modality preservation module to effectively embed each modality into a common latent space, UniTCR demonstrates versatility in connecting TCR sequences with T cell transcriptomes across various tasks, including single-modality analysis, modality gap analysis, epitope-TCR binding prediction, and TCR profile cross-modality generation, in a low-resource-aware way. Extensive evaluations conducted on multiple scRNA-seq/TCR-seq paired datasets showed the superior performance of UniTCR, exhibiting the ability of exploring the complexity of immune system.

Can AlphaFold's breakthrough in protein structure help decode the fundamental principles of adaptive cellular immunity?

T cells are essential immune cells responsible for identifying and eliminating pathogens. Through interactions between their T-cell antigen receptors (TCRs) and antigens presented by major histocompatibility complex molecules (MHCs) or MHC-like molecules, T cells discriminate foreign and self peptides. Determining the fundamental principles that govern these interactions has important implications in numerous medical contexts. However, reconstructing a map between T cells and their antagonist antigens remains an open challenge for the field of immunology, and success of in silico reconstructions of this relationship has remained incremental. In this Perspective, we discuss the role that new state-of-the-art deep-learning models for predicting protein structure may play in resolving some of the unanswered questions the field faces linking TCR and peptide-MHC properties to T-cell specificity. We provide a comprehensive overview of structural databases and the evolution of predictive models, and highlight the breakthrough AlphaFold provided the field.

Principles and therapeutic applications of adaptive immunity

Adaptive immunity provides protection against infectious and malignant diseases. These effects are mediated by lymphocytes that sense and respond with targeted precision to perturbations induced by pathogens and tissue damage. Here, we review key principles underlying adaptive immunity orchestrated by distinct T cell and B cell populations and their extensions to disease therapies. We discuss the intracellular and intercellular processes shaping antigen specificity and recognition in immune activation and lymphocyte functions in mediating effector and memory responses. We also describe how lymphocytes balance protective immunity against autoimmunity and immunopathology, including during immune tolerance, response to chronic antigen stimulation, and adaptation to non-lymphoid tissues in coordinating tissue immunity and homeostasis. Finally, we discuss extracellular signals and cell-intrinsic programs underpinning adaptive immunity and conclude by summarizing key advances in vaccination and engineering adaptive immune responses for therapeutic interventions. A deeper understanding of these principles holds promise for uncovering new means to improve human health.

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.

Deciphering Membrane-Protein Interactions and High-Throughput Antigen Identification with Cell Doublets

Deciphering cellular interactions is essential to both understand the mechanisms underlying a broad range of human diseases, but also to manipulate therapies targeting these diseases. Here, the formation of cell doublets resulting from specific membrane ligand-receptor interactions is discovered. Based on this phenomenon, the study developed DoubletSeeker, a novel high-throughput method for the reliable identification of ligand-receptor interactions. The study shows that DoubletSeeker can accurately identify T cell receptor (TCR)-antigen interactions with high sensitivity and specificity. Notably, DoubletSeeker effectively captured paired TCR-peptide major histocompatibility complex (pMHC) information during a highly complex library-on-library screening and successfully identified three mutant TCRs that specifically recognize the MART-1 epitope. In turn, DoubletSeeker can act as an antigen discovery platform that allows for the development of novel immunotherapy targets, making it valuable for investigating fundamental tumor immunology.

Predicting TCR sequences for unseen antigen epitopes using structural and sequence features

T-cell receptor (TCR) recognition of antigens is fundamental to the adaptive immune response. With the expansion of experimental techniques, a substantial database of matched TCR-antigen pairs has emerged, presenting opportunities for computational prediction models. However, accurately forecasting the binding affinities of unseen antigen-TCR pairs remains a major challenge. Here, we present convolutional-self-attention TCR (CATCR), a novel framework tailored to enhance the prediction of epitope and TCR interactions. Our approach utilizes convolutional neural networks to extract peptide features from residue contact matrices, as generated by OpenFold, and a transformer to encode segment-based coded sequences. We introduce CATCR-D, a discriminator that can assess binding by analyzing the structural and sequence features of epitopes and CDR3-β regions. Additionally, the framework comprises CATCR-G, a generative module designed for CDR3-β sequences, which applies the pretrained encoder to deduce epitope characteristics and a transformer decoder for predicting matching CDR3-β sequences. CATCR-D achieved an AUROC of 0.89 on previously unseen epitope-TCR pairs and outperformed four benchmark models by a margin of 17.4%. CATCR-G has demonstrated high precision, recall and F1 scores, surpassing 95% in bidirectional encoder representations from transformers score assessments. Our results indicate that CATCR is an effective tool for predicting unseen epitope-TCR interactions. Incorporating structural insights enhances our understanding of the general rules governing TCR-epitope recognition significantly. The ability to predict TCRs for novel epitopes using structural and sequence information is promising, and broadening the repository of experimental TCR-epitope data could further improve the precision of epitope-TCR binding predictions.

Machine learning predictions of T cell antigen specificity from intracellular calcium dynamics

Adoptive T cell therapies rely on the production of T cells with an antigen receptor that directs their specificity toward tumor-specific antigens. Methods for identifying relevant T cell receptor (TCR) sequences, predominantly achieved through the enrichment of antigen-specific T cells, represent a major bottleneck in the production of TCR-engineered cell therapies. Fluctuation of intracellular calcium is a proximal readout of TCR signaling and candidate marker for antigen-specific T cell identification that does not require T cell expansion; however, calcium fluctuations downstream of TCR engagement are highly variable. We propose that machine learning algorithms may allow for T cell classification from complex datasets such as polyclonal T cell signaling events. Using deep learning tools, we demonstrate accurate prediction of TCR-transgenic CD8+ T cell activation based on calcium fluctuations and test the algorithm against T cells bearing a distinct TCR as well as polyclonal T cells. This provides the foundation for an antigen-specific TCR sequence identification pipeline for adoptive T cell therapies.

Enhancing TCR specificity predictions by combined pan- and peptide-specific training, loss-scaling, and sequence similarity integration

Predicting the interaction between Major Histocompatibility Complex (MHC) class I-presented peptides and T-cell receptors (TCR) holds significant implications for vaccine development, cancer treatment, and autoimmune disease therapies. However, limited paired-chain TCR data, skewed towards well-studied epitopes, hampers the development of pan-specific machine-learning (ML) models. Leveraging a larger peptide-TCR dataset, we explore various alterations to the ML architectures and training strategies to address data imbalance. This leads to an overall improved performance, particularly for peptides with scant TCR data. However, challenges persist for unseen peptides, especially those distant from training examples. We demonstrate that such ML models can be used to detect potential outliers, which when removed from training, leads to augmented performance. Integrating pan-specific and peptide-specific models alongside with similarity-based predictions, further improves the overall performance, especially when a low false positive rate is desirable. In the context of the IMMREP22 benchmark, this modeling framework attained state-of-the-art performance. Moreover, combining these strategies results in acceptable predictive accuracy for peptides characterized with as little as 15 positive TCRs. This observation places great promise on rapidly expanding the peptide covering of the current models for predicting TCR specificity. The NetTCR 2.2 model incorporating these advances is available on GitHub (https://github.com/mnielLab/NetTCR-2.2) and as a web server at https://services.healthtech.dtu.dk/services/NetTCR-2.2/.

A comparison of clustering models for inference of T cell receptor antigen specificity

The vast potential sequence diversity of TCRs and their ligands has presented an historic barrier to computational prediction of TCR epitope specificity, a holy grail of quantitative immunology. One common approach is to cluster sequences together, on the assumption that similar receptors bind similar epitopes. Here, we provide the first independent evaluation of widely used clustering algorithms for TCR specificity inference, observing some variability in predictive performance between models, and marked differences in scalability. Despite these differences, we find that different algorithms produce clusters with high degrees of similarity for receptors recognising the same epitope. Our analysis strengthens the case for use of clustering models to identify signals of common specificity from large repertoires, whilst highlighting scope for improvement of complex models over simple comparators.

Integrating single-cell multi-omics and prior biological knowledge for a functional characterization of the immune system

The immune system comprises diverse specialized cell types that cooperate to defend the host against a wide range of pathogenic threats. Recent advancements in single-cell and spatial multi-omics technologies provide rich information about the molecular state of immune cells. Here, we review how the integration of single-cell and spatial multi-omics data with prior knowledge-gathered from decades of detailed biochemical studies-allows us to obtain functional insights, focusing on gene regulatory processes and cell-cell interactions. We present diverse applications in immunology and critically assess underlying assumptions and limitations. Finally, we offer a perspective on the ongoing technological and algorithmic developments that promise to get us closer to a systemic mechanistic understanding of the immune system.

Niche-specific control of tissue function by regulatory T cells-Current challenges and perspectives for targeting metabolic disease

Tissue regulatory T cells (Tregs) exert pivotal functions in both immune and metabolic regulation, maintaining local tissue homeostasis, integrity, and function. Accordingly, Tregs play a crucial role in controlling obesity-induced inflammation and supporting efficient muscle function and repair. Depending on the tissue context, Tregs are characterized by unique transcriptomes, growth, and survival factors and T cell receptor (TCR) repertoires. This functional specialization offers the potential to selectively target context-specific Treg populations, tailoring therapeutic strategies to specific niches, thereby minimizing potential side effects. Here, we discuss challenges and perspectives for niche-specific Treg targeting, which holds promise for highly efficient and precise medical interventions to combat metabolic disease.