Inference of SARS-CoV-2 exposure biomarkers using large-scale T-cell repertoire profiling
| Title: | Inference of SARS-CoV-2 exposure biomarkers using large-scale T-cell repertoire profiling |
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| Authors: | 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; Anastasia Pivnyuk; Pavel V. Shelyakin; Ilgar Z. Mamedov; Denis V. Rebrikov; Sergey M. Yudin; Veronika I. Skvortsova; Dmitry M. Chudakov; Olga V. Britanova; Mikhail Shugay |
| Source: | Genome Medicine, Vol 18, Iss 1 (2026) |
| Publisher Information: | BMC |
| Publication Year: | 2026 |
| Collection: | Directory of Open Access Journals: DOAJ Articles |
| Subject Terms: | T cell receptor; Immune repertoires; Immune biomarkers; COVID-19; TCR specificity; Phenotype prediction; Medicine; Genetics; QH426-470 |
| Description: | Background The 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. Methods The 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. Results Here, 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. Conclusions This 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. |
| Document Type: | article in journal/newspaper |
| Language: | English |
| Relation: | https://doi.org/10.1186/s13073-025-01589-4; https://doaj.org/toc/1756-994X; https://doaj.org/article/1ffed64d5f154b43b59d2bf830053087 |
| DOI: | 10.1186/s13073-025-01589-4 |
| Availability: | https://doi.org/10.1186/s13073-025-01589-4; https://doaj.org/article/1ffed64d5f154b43b59d2bf830053087 |
| Accession Number: | edsbas.B623832E |
| Database: | BASE |