Evidence map›Paper›PMID 41680899›Full record

ArticleGenome medicine2026

Inference of SARS-CoV-2 exposure biomarkers using large-scale T-cell repertoire profiling.

Elizaveta K Vlasova, Alexandra I Nekrasova, Alexander Y Komkov, Mark Izraelson, Ekaterina A Snigir, Sergey I Mitrofanov, Vladimir S Yudin, Valentin V Makarov, Anton A Keskinov, Darya Korneeva and 9 more

Abstract read
In one paragraph

Article in Genome medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. T Cell Thoughts.Immunological reviews · 2026
    Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Elizaveta K VlasovaInstitute of Translational Medicine, Pirogov Russian National Research Medical University, Ostrovityanova Ulitsa 1 Bldg. 1, Moscow, 117513, Russia.
Alexandra I NekrasovaFederal State Budgetary Institution "Centre for Strategic Planning and Management of Biomedical Health Risks", Federal Medical Biological Agency, Moscow, Russia.
Alexander Y KomkovDepartment of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.
Mark IzraelsonDepartment of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.
Ekaterina A SnigirFederal State Budgetary Institution "Centre for Strategic Planning and Management of Biomedical Health Risks", Federal Medical Biological Agency, Moscow, Russia.
Sergey I MitrofanovFederal State Budgetary Institution "Centre for Strategic Planning and Management of Biomedical Health Risks", Federal Medical Biological Agency, Moscow, Russia.
Vladimir S YudinFederal State Budgetary Institution "Centre for Strategic Planning and Management of Biomedical Health Risks", Federal Medical Biological Agency, Moscow, Russia.
Valentin V MakarovFederal State Budgetary Institution "Centre for Strategic Planning and Management of Biomedical Health Risks", Federal Medical Biological Agency, Moscow, Russia.
Anton A KeskinovFederal State Budgetary Institution "Centre for Strategic Planning and Management of Biomedical Health Risks", Federal Medical Biological Agency, Moscow, Russia.
Darya KorneevaInstitute of Translational Medicine, Pirogov Russian National Research Medical University, Ostrovityanova Ulitsa 1 Bldg. 1, Moscow, 117513, Russia.
Anastasia PivnyukInstitute of Translational Medicine, Pirogov Russian National Research Medical University, Ostrovityanova Ulitsa 1 Bldg. 1, Moscow, 117513, Russia.
Pavel V ShelyakinDepartment of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.
Ilgar Z MamedovDepartment of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.
Denis V RebrikovInstitute of Translational Medicine, Pirogov Russian National Research Medical University, Ostrovityanova Ulitsa 1 Bldg. 1, Moscow, 117513, Russia.
Sergey M YudinFederal State Budgetary Institution "Centre for Strategic Planning and Management of Biomedical Health Risks", Federal Medical Biological Agency, Moscow, Russia.
Veronika I SkvortsovaInstitute of Translational Medicine, Pirogov Russian National Research Medical University, Ostrovityanova Ulitsa 1 Bldg. 1, Moscow, 117513, Russia.
Dmitry M ChudakovInstitute of Translational Medicine, Pirogov Russian National Research Medical University, Ostrovityanova Ulitsa 1 Bldg. 1, Moscow, 117513, Russia. chudakovdm@gmail.com.
Olga V BritanovaInstitute of Translational Medicine, Pirogov Russian National Research Medical University, Ostrovityanova Ulitsa 1 Bldg. 1, Moscow, 117513, Russia. olbritan@gmail.com.
Mikhail ShugayInstitute of Translational Medicine, Pirogov Russian National Research Medical University, Ostrovityanova Ulitsa 1 Bldg. 1, Moscow, 117513, Russia. mikhail.shugay@gmail.com.

Funding

Russian Science Foundation Grant №25-75-30013
6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic offers a powerful opportunity to develop methods for monitoring the spread of infectious diseases based on their signatures in population immunity. Adaptive immune receptor repertoire sequencing (AIRR-seq) has become the method of choice for identifying T cell receptor (TCR) biomarkers encoding pathogen specificity and immunological memory. AIRR-seq can detect imprints of past and ongoing infections and facilitate the study of individual responses to SARS-CoV-2, as shown in many recent studies.

methodsThe new batch effect correction method allowed us to use data from different batches together, as well as combine the analysis for data obtained using different protocols. Proper standardization of AIRR-seq batches, access to human leukocyte antigen (HLA) typing, and the use of both α- and β-chain sequences of TCRs resulted in a high-quality biomarker database and a robust and highly accurate classifier for COVID-19 exposure.

resultsHere, we have applied a machine learning approach to two large AIRR-seq datasets with more than 1,200 high-quality repertoires from healthy and COVID-19-convalescent donors to infer TCR repertoire features that were induced by SARS-CoV-2 exposure.

conclusionsThis developed classifier is applicable to individual TCR repertoires obtained using various protocols, paving the way to AIRR-seq-based immune status assessment in large cohorts of donors.

Indexed as

COVID-19Receptors, Antigen, T-CellT-LymphocytesBiomarkersHumansMachine LearningPandemicsSARS-CoV-2BiomarkersReceptors, Antigen, T-CellCOVID-19Immune biomarkersImmune repertoiresPhenotype predictionT cell receptorTCR specificity

Identifiers

PMID41680899
PMCPMC12903587

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.