Author

A. A. Minervina

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Biology Disciplines

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Immunology· 8

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 8 Organism:human· 8 Approach:statistical model· 7 Assay:scRNA-seq· 5 Research Focus:cell-cell communication· 3 Approach:deep learning· 3 Research Focus:differential expression· 3 Assay:CITE-seq· 2

Frequent Co-Authors

M. V. Pogorelyy· 8 shared D. M. Chudakov· 5 shared T. Mora· 5 shared Y. B. Lebedev· 5 shared A. M. Walczak· 4 shared E. Rosati· 4 shared I. Z. Mamedov· 4 shared A. Franke· 3 shared

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2017-2023 · peak 2/yr

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10 top collaborators · 45 mutual co-author links

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8 shared papersImmunology

M. V. Pogorelyy

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8 papers

Recent·Most cited

Identification of Antigen-Specific T Cell Receptors with combinatorial peptide pooling

V. A. Kovaleva, D. J. Pattinson, G. He, C. Barton, S. R. Chapin, A. A. Minervina, Q. Huang, P. G. Thomas, M. V. Pogorelyy, H. V. Meyer

This paper introduces copepodTCR, a tool for designing and interpreting TCR specificity assays, which efficiently identifies antigen-specific TCRs using combinatorial peptide pooling and Bayesian modeling.

bioRxiv · Nov 28, 2023 · PreprintImmunology

Characterization of SARS-CoV-2 public CD4+ αβ T cell clonotypes through reverse epitope discovery

E. Rosati, M. V. Pogorelyy, A. A. Minervina, A. Franke, A. Scheffold, P. Bacher, P. Thomas

The paper identifies public CD4+ T cell clonotypes reactive to SARS-CoV-2 and predicts their HLA-restriction and epitopes using reverse epitope discovery.

bioRxiv · Nov 19, 2021Immunology

SARS-CoV-2 mRNA vaccination elicits robust and persistent T follicular helper cell response in humans

P. A. Mudd, A. A. Minervina, M. V. Pogorelyy, J. S. Turner, W. Kim, E. Kalaidina, J. Petersen, A. J. Schmitz, T. Lei, A. Haile, A. M. Kirk, R. C. Mettelman, J. C. Crawford, T. H. O. Nguyen, L. C. Rowntree, , A. Ellebedy

This study shows that SARS-CoV-2 mRNA vaccination leads to strong and long-lasting T follicular helper cell responses in humans.

bioRxiv · Sep 8, 2021Immunology

Longitudinal high-throughput TCR repertoire profiling reveals the dynamics of T cell memory formation after mild COVID-19 infection

A. A. Minervina, E. A. Komech, A. Titov, M. Bensouda Koraichi, E. Rosati, I. Z. Mamedov, A. Franke, G. A. Efimov, D. M. Chudakov, T. Mora, A. M. Walczak, Y. B. Lebedev, M. V. Pogorelyy

This study tracks T cell memory formation after mild COVID-19 infection using longitudinal TCR sequencing, showing clonal expansion and memory phenotype acquisition in infected individuals.

bioRxiv · May 18, 2020Immunology

Comprehensive analysis of antiviral adaptive immunity formation and reactivation down to single-cell level

A. A. Minervina, M. V. Pogorelyy, E. A. Komech, V. K. Karnauhov, P. Bacher, E. Rosati, A. Franke, D. M. Chudakov, I. Z. Mamedov, Y. B. Lebedev, T. Mora, A. M. Walczak

The paper tracks T-cell clone dynamics and TCR repertoire changes after primary and secondary yellow fever vaccination using single-cell RNAseq and TCRseq.

bioRxiv · Oct 25, 2019 · PreprintImmunology

Detecting T-cell receptors involved in immune responses from single repertoire snapshots

M. V. Pogorelyy, A. A. Minervina, M. Shugay, D. M. Chudakov, Y. B. Lebedev, T. Mora, A. M. Walczak

This paper introduces ALICE, a method to identify TCRs involved in immune responses from single RepSeq samples, showing its effectiveness across various disease conditions.

bioRxiv · Jul 23, 2018Immunology

Precise tracking of vaccine-responding T-cell clones reveals convergent and personalized response in identical twins

M. V. Pogorelyy, A. A. Minervina, M. Puelma Touzel, A. L. Sycheva, E. A. Komech, E. I. Kovalenko, G. G. Karganova, E. S. Egorov, A. Y. Komkov, D. M. Chudakov, I. Z. Mamedov, T. Mora, A. Walczak, Y. B. Lebedev

The paper develops a statistical framework to detect TCR clone changes in vaccinated twins and finds convergent yet personalized TCR responses.

bioRxiv · Apr 13, 2018Immunology

Method for identification of condition-associated public antigen receptor sequences

M. V. Pogorelyy, A. A. Minervina, D. M. Chudakov, I. Z. Mamedov, Y. B. Lebedev, T. Mora, A. M. Walczak

The paper develops a statistical framework to associate antigen receptor sequences with diseases using a small patient cohort, successfully identifying validated receptors for Cytomegalovirus and type 1 diabetes.

bioRxiv · Sep 28, 2017Immunology