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

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Generation of peptide–MHC class I complexes through UV-mediated ligand exchange

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Systematic identification of personal tumor-specific neoantigens in chronic lymphocytic leukemia

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Clonal Deletion Prunes but Does Not Eliminate Self-Specific αβ CD8+ T Lymphocytes

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T Cell Receptor Cross-Reactivity between Similar Foreign and Self Peptides Influences Naive Cell Population Size and Autoimmunity

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Beyond model antigens: high-dimensional methods for the analysis of antigen-specific T cells

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Manipulation of Avidity to Improve Effectiveness of Adoptively Transferred CD8+ T Cells for Melanoma Immunotherapy in Human MHC Class I-Transgenic Mice

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On the PAM matrix model of protein evolution.

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T cell receptor alpha variable 12‐2 bias in the immunodominant response to Yellow fever virus

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Digital Encoding of Cellular mRNAs Enabling Precise and Absolute Gene Expression Measurement by Single-Molecule Counting

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T cells in health and disease

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Dynamics and specificities of T cells in cancer immunotherapy

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Guidelines for the use of flow cytometry and cell sorting in immunological studies (third edition)

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Analyzing the Mycobacterium tuberculosis immune response by T-cell receptor clustering with GLIPH2 and genome-wide antigen screening

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Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing

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TCR-engineered T cell therapy in solid tumors: State of the art and perspectives

Baulu, E., Gardet, C., Chuvin, N., & Depil, S. (2023) · Science Advances · doi:10.1126/sciadv.adf3700 · Cited by 439 · see citing works

High-Throughput Mapping of B Cell Receptor Sequences to Antigen Specificity

Setliff, I., Shiakolas, A. R., Pilewski, K. A., Murji, A. A., Mapengo, R. E., Janowska, K., Richardson, S., Oosthuysen, C., Raju, N., Ronsard, L., et al. (2019) · Cell · doi:10.1016/j.cell.2019.11.003 · Cited by 314 · see citing works

Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy

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

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Deep learning-based prediction of the T cell receptor–antigen binding specificity

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Antitumour dendritic cell vaccination in a priming and boosting approach

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T-Scan: A Genome-wide Method for the Systematic Discovery of T Cell Epitopes

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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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Single-cell sequencing to multi-omics: technologies and applications

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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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Antigen identification and high-throughput interaction mapping by reprogramming viral entry

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Implications of antibody-dependent enhancement of infection for SARS-CoV-2 countermeasures

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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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Augmenting adaptive immunity: progress and challenges in the quantitative engineering and analysis of adaptive immune receptor repertoires

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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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TCR-sequencing in cancer and autoimmunity: barcodes and beyond

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Adaptive immune receptor repertoire analysis

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Recent advances in T-cell receptor repertoire analysis: Bridging the gap with multimodal single-cell RNA sequencing

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T-Cell Receptor Cognate Target Prediction Based on Paired α and β Chain Sequence and Structural CDR Loop Similarities

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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

Wang, S., Liu, Y., Li, Y., Lv, M., Gao, K., He, Y., Wei, W., Zhu, Y., Dong, X., Xu, X., et al. (2021) · Analytical Chemistry · doi:10.1021/acs.analchem.1c03678 · Cited by 40 · see citing works

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

Trück, J., Eugster, A., Barennes, P., Tipton, C. M., Prak, E. T. L., Bagnara, D., Soto, C. S., Sherkow, J. S., Payne, A. S., Lefranc, M., et al. (2021) · eLife · doi:10.7554/elife.66274 · Cited by 37 · see citing works

Single-Cell Analysis in Immuno-Oncology

Christodoulou, M., & Zaravinos, A. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24098422 · Cited by 35 · see citing works

SITC cancer immunotherapy resource document: a compass in the land of biomarker discovery

Hu‐Lieskovan, S., Bhaumik, S., Dhodapkar, K. M., Grivel, J., Gupta, S. V., Hanks, B. A., Janetzki, S., Kleen, T. O., Koguchi, Y., Lund, A. W., et al. (2020) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2020-000705 · Cited by 35 · see citing works

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

Kast, F., Klein, C., Umaña, P., Gros, A., & Gasser, S. (2021) · OncoImmunology · doi:10.1080/2162402x.2020.1869389 · Cited by 34 · see citing works

Determining epitope specificity of T cell receptors with TCRGPPreprint

Jokinen, E., Huuhtanen, J., Mustjoki, S. M., Heinonen, M., & Lähdesmäki, H. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/542332 · Cited by 38 · see citing works

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

Koo, D., Mao, Z., Dimatteo, R., Noguchi, M., Tsubamoto, N., McLaughlin, J., Tran, W., Lee, S., Cheng, D., de Rutte, J. M., et al. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2320442121 · Cited by 33 · see citing works

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

Zvyagin, I. V., Tsvetkov, V. O., Chudakov, D. M., & Shugay, M. (2019) · Immunogenetics · doi:10.1007/s00251-019-01139-4 · Cited by 32 · see citing works

Biotechnologies to tackle the challenge of neoantigen identification

Arnaud, M., Duchamp, M., Bobisse, S., Renaud, P., Coukos, G., & Harari, A. (2020) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2019.12.014 · Cited by 33 · see citing works

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

Prinz, J. C. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1097871 · Cited by 31 · see citing works

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

Zhang, Y., Wang, Z., Jiang, Y., Littler, D. R., Gerstein, M., Purcell, A. W., Rossjohn, J., Ou, H., & Song, J. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00913-8 · Cited by 31 · see citing works

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

Bentzen, A. K., & Hadrup, S. R. (2019) · Immuno-Oncology Technology · doi:10.1016/j.iotech.2019.06.003 · Cited by 31 · see citing works

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

Feng, Y., Zhao, X., White, A. K., García, K. C., & Fordyce, P. M. (2022) · Nature Methods · doi:10.1038/s41592-022-01592-2 · Cited by 29 · see citing works

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

Shao, W., Yao, Y., Yang, L., Li, X., Ge, T., Zheng, Y., Zhu, Q., Ge, S., Gu, X., Jia, R., et al. (2024) · Experimental Hematology and Oncology · doi:10.1186/s40164-024-00504-8 · Cited by 29 · see citing works

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

Trofimov, A., Brouillard, P., Larouche, J., Séguin, J., Laverdure, J., Brasey, A., Ehx, G., Roy, D., Busque, L., Lachance, S., et al. (2022) · iScience · doi:10.1016/j.isci.2022.104968 · Cited by 28 · see citing works

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 25 · see citing works

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 · see citing works

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 24 · see citing works

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 23 · see citing works

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 23 · see citing works

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 22 · see citing works

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 23 · see citing works

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 21 · see citing works

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 24 · see citing works

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 21 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 17 · see citing works

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 17 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 14 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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