High-throughput determination of the antigen specificities of T cell receptors in single cells

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Alternative mRNA splicing in cancer immunotherapy

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Prediction of Specific TCR-Peptide Binding From Large Dictionaries of TCR-Peptide Pairs

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

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Targeting public neoantigens for cancer immunotherapy

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T cell receptor therapeutics: immunological targeting of the intracellular cancer proteome

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Bystander T cells in cancer immunology and therapy

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Pan-Peptide Meta Learning for T-cell receptor–antigen binding recognition

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T cell antigen discovery

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Lentiviral Vectors for T Cell Engineering: Clinical Applications, Bioprocessing and Future Perspectives

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Predicting recognition between T cell receptors and epitopes with TCRGP

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Computational Prediction and Validation of Tumor-Associated Neoantigens

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GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation

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Immunoinformatics: Predicting Peptide–MHC Binding

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DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor

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Acoustic tweezers for high-throughput single-cell analysis

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De novo prediction of cancer-associated T cell receptors for noninvasive cancer detection

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Sensitive Detection and Analysis of Neoantigen-Specific T Cell Populations from Tumors and Blood

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Engineered cell entry links receptor biology with single-cell genomics

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Harnessing 3D in vitro systems to model immune responses to solid tumours: a step towards improving and creating personalized immunotherapies

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T-Cell Receptor Repertoire Sequencing in the Era of Cancer Immunotherapy

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T‐cell receptor and B‐cell receptor repertoire profiling in adaptive immunity

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Primary and secondary anti-viral response captured by the dynamics and phenotype of individual T cell clones

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Nanoscale organization of two-dimensional multimeric pMHC reagents with DNA origami for CD8+ T cell detection

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Predicting Cross-Reactivity and Antigen Specificity of T Cell Receptors

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Direct identification of neoantigen-specific TCRs from tumor specimens by high-throughput single-cell sequencing

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Decoding Cancer Biology One Cell at a Time

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High throughput pMHC-I tetramer library production using chaperone-mediated peptide exchange

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Adaptive immune receptor repertoire analysis

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T Cells: A Growing Universe of Roles in Neurodegenerative Diseases

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Applications of Single-Cell Omics in Tumor Immunology

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Adoptive Cell Therapy—Harnessing Antigen-Specific T Cells to Target Solid Tumours

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

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Neoantigen-Specific Adoptive Cell Therapies for Cancer: Making T-Cell Products More Personal

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MATE-Seq: microfluidic antigen-TCR engagement sequencing

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The Diagnostic, Prognostic, and Therapeutic Potential of Adaptive Immune Receptor Repertoire Profiling in Cancer

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Identification of the Targets of T-cell Receptor Therapeutic Agents and Cells by Use of a High-Throughput Genetic Platform

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Antigen-specificity measurements are the key to understanding T cell responses

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Improved T cell receptor antigen pairing through data-driven filtering of sequencing information from single cells

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High-Throughput Functional Screening of Antigen-Specific T Cells Based on Droplet Microfluidics at a Single-Cell Level

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Biological controls for standardization and interpretation of adaptive immune receptor repertoire profiling

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Single-Cell Analysis in Immuno-Oncology

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Advances in identification and selection of personalized neoantigen/T-cell pairs for autologous adoptive T cell therapies

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Determining epitope specificity of T cell receptors with TCRGPPreprint

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Defining T cell receptor repertoires using nanovial-based binding and functional screening

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An overview of immunoinformatics approaches and databases linking T cell receptor repertoires to their antigen specificity

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Immunogenic self-peptides - the great unknowns in autoimmunity: Identifying T-cell epitopes driving the autoimmune response in autoimmune diseases

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Epitope-anchored contrastive transfer learning for paired CD8+ T cell receptor–antigen recognition

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T-cell-receptor cross-recognition and strategies to select safe T-cell receptors for clinical translation

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A bead-based method for high-throughput mapping of the sequence- and force-dependence of T cell activation

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Novel insights into TCR-T cell therapy in solid neoplasms: optimizing adoptive immunotherapy

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Two types of human TCR differentially regulate reactivity to self and non-self antigens

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TCR sequencing: applications in immuno-oncology research

Fernández-Sanromán, Á., Joshi, K., Au, L., Chain, B., & Turajlic, S. (2023) · Immuno-Oncology Technology · doi:10.1016/j.iotech.2023.100373 · Cited by 26

De novo identification of CD4+ T cell epitopes

Zdinak, P. M., Trivedi, N., Grebinoski, S., Torrey, J., Martinez, E. Z., Martínez, S., Hicks, L., Ranjan, R., Makani, V. K. K., Roland, M. M., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02255-0 · Cited by 26

Antigen‐specific and cross‐reactive T cells in protection and disease

Jiang, N. J., Malone, M. D., & Chizari, S. (2023) · Immunological Reviews · doi:10.1111/imr.13217 · Cited by 25

BERTrand—peptide:TCR binding prediction using Bidirectional Encoder Representations from Transformers augmented with random TCR pairing

Myronov, A., Mazzocco, G., Król, P., & Plewczyński, D. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad468 · Cited by 24

Ushering in Integrated T Cell Repertoire Profiling in Cancer

Jiang, N. J., Schonnesen, A. A., & Ma, K.-Y. (2018) · Trends in cancer · doi:10.1016/j.trecan.2018.11.005 · Cited by 25

Single-Cell TCR and Transcriptome Analysis: An Indispensable Tool for Studying T-Cell Biology and Cancer Immunotherapy

Pasetto, A., & Lu, Y. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.689091 · Cited by 24

Prediction of cancer neoepitopes needs new rules

Brennick, C. A., George, M. M., Srivastava, P. K., & Karandikar, S. H. (2020) · Seminars in Immunology · doi:10.1016/j.smim.2020.101387 · Cited by 22

Neoantigen‐reactive T cell: An emerging role in adoptive cellular immunotherapy

Zhu, Y., Qian, Y., Li, Z., Li, Y., & Li, B. (2021) · MedComm · doi:10.1002/mco2.41 · Cited by 23

DeepTCR: a deep learning framework for understanding T-cell receptor sequence signatures within complex T-cell repertoiresPreprint

Sidhom, J.-W., Larman, H. B., Ross-MacDonald, P., Wind-Rotolo, M., Pardoll, D. M., & Baras, A. S. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/464107 · Cited by 25

Identifying T cell antigen at the atomic level with graph convolutional network

Que, J., Xue, G., Wang, T., Jin, X., Wang, Z., Cai, Y., Yang, W., Luo, M., Ding, Q., Zhang, J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60461-6 · Cited by 25

Assessment of computational methods in predicting TCR–epitope binding recognition

Lu, Y. E., Wang, Y., Xu, M., Xie, B., Yang, Y., Xu, H., & Suo, S. (2025) · Nature Methods · doi:10.1038/s41592-025-02910-0 · Cited by 24

Natural Barcodes for Longitudinal Single Cell Tracking of Leukemic and Immune Cell Dynamics

Penter, L., Gohil, S. H., & Wu, C. J. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2021.788891 · Cited by 20

The physiological interactome of TCR-like antibody therapeutics in human tissues

Marrer‐Berger, E., Nicastri, A., Augustin, A., Kramar, V., Liao, H., Hanisch, L. J., Carpy, A., Weinzierl, T., Durr, E., Schaub, N. A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47062-5 · Cited by 20

A unified cross-attention model for predicting antigen binding specificity to both HLA and TCR molecules

Yu, C., Fang, X., Tian, S., & Liu, H. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-024-00973-w · Cited by 20

GRAPE: graph-regularized protein language modeling unlocks TCR-epitope binding specificity

Fu, X., Li, P., Chen, H., Rong, M., Chen, Y., Cao, D., Yuan, S., & Lü, A. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf522 · Cited by 18

Multiomics Empowers Predictive Pancreatic Cancer Immunotherapy

Montagne, J. M., Jaffee, E. M., & Fertig, E. J. (2023) · The Journal of Immunology · doi:10.4049/jimmunol.2200660 · Cited by 18

Simultaneous analysis of pMHC binding and reactivity unveils virus-specific CD8 T cell immunity to a concise epitope set

Kristensen, N. P., Dionisio, E., Bentzen, A. K., Tamhane, T., Kemming, J. S., Nos, G., Voss, L. F., Hansen, U. K., Lauer, G. M., & Hadrup, S. R. (2024) · Science Advances · doi:10.1126/sciadv.adm8951 · Cited by 19

MIX-TPI: a flexible prediction framework for TCR–pMHC interactions based on multimodal representations

Yang, M., Huang, Z.-A., Zhou, W., Ji, J., Zhang, J., He, S., & Zhu, Z. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad475 · Cited by 20

Immune environment and antigen specificity of the T cell receptor repertoire of malignant ascites in ovarian cancer

Yoshida-Court, K., Karpinets, T. V., Mitra, A., Solley, T. N., Dorta-Estremera, S. M., Sims, T. T., Medrano, A. Y. D., Alam, M. B. E., Ahmed-Kaddar, M., Lynn, E. J., et al. (2023) · PLoS ONE · doi:10.1371/journal.pone.0279590 · Cited by 16

GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity

Jiang, F., Guo, Y., Ma, H., Na, S., Zhong, W., Han, Y., Wang, T., & Huang, J. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae343 · Cited by 17

T cell receptor specificity landscape revealed through de novo peptide design

Visani, G. M., Pun, M. N., Minervina, A. A., Bradley, P. H., Thomas, P. G., & Nourmohammad, A. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2504783122 · Cited by 17

iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features

Zhang, Y., Jian, X., Xu, L., Zhao, J., Lu, M., Lin, Y., & Xie, L. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1141535 · Cited by 15

Characterization of CD8 + T-cell responses to non-anchor-type HLA class I neoantigens with single amino-acid substitutions

Shinkawa, T., Tokita, S., Nakatsugawa, M., Kikuchi, Y., Kanaseki, T., & Torigoe, T. (2021) · OncoImmunology · doi:10.1080/2162402x.2020.1870062 · Cited by 15

Utilizing immunogenomic approaches to prioritize targetable neoantigens for personalized cancer immunotherapy

Shah, R. K., Cygan, E. E., Kozlik, T., Colina, A. S., & Zamora, A. E. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1301100 · Cited by 15

High-throughput single-cell linking of antigen specificities with T cell receptor sequences using de novo generated DNA-linked MHC tetramers

Jiang, N. J. (2018) · The Journal of Immunology · doi:10.4049/jimmunol.200.supp.120.23

Abstract 4678: High-throughput single-cell linking of antigen specificities with T cell receptor sequences using de novo generated DNA-linked MHC tetramers

Jiang, N. J., Zhang, S., He, C., Sun, E., Zhang, M., & Jia, W. (2018) · Cancer Research · doi:10.1158/1538-7445.am2018-4678

Direct identification of neoantigen-specific TCRs from tumor specimens by high-throughput single-cell sequencing

Lu, Y., Zheng, Z., Lowery, F. J., Gartner, J. J., Prickett, T. D., Robbins, P. F., & Rosenberg, S. A. (2021) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2021-002595 · Cited by 63

Tetramer-Associated T Cell Receptor Sequencing

Malone, M. D., Ma, K.-Y., Zhang, S., & Jiang, N. J. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2712-9_8 · Cited by 2

Abstract 4066: High throughput single-cell TCR sequencing method identifies cancer-specific TCR clonotypes

Papalexi, E., Curca, C., Hariadi, B., Suyama, A., Marrujo, S. R., Geiss, G. K., Roco, C. M., & Rosenberg, A. (2025) · Cancer Research · doi:10.1158/1538-7445.am2025-4066