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Inferencia de los biomarcadores de exposición al SARS-CoV-2 utilizando el perfil del repertorio de células T a gran

Elizaveta K Vlasova^1,2, Alexandra I Nekrasova³, Alexander Y Komkov^4,5,6

¹Institute of Translational Medicine, Pirogov Russian National Research Medical University, Ostrovityanova Ulitsa 1 Bldg. 1, Moscow, 117513, Russia.

Genome medicine

|February 12, 2026

Resumen

Un nuevo clasificador de aprendizaje automático identifica con precisión la exposición a COVID-19 mediante el análisis de los repertorios de receptores de células T (TCR). Este método utiliza datos de secuenciación adaptativa de receptores inmunes (AIRR-seq) para evaluar el estado inmune en grandes poblaciones.

Área de la Ciencia:

Inmunología Inmunología.
Epidemiología de las enfermedades infecciosas.
La bioinformática es la bioinformática.

Sus antecedentes:

La pandemia de COVID-19 pone de relieve la necesidad de métodos para rastrear la propagación de enfermedades infecciosas utilizando firmas de inmunidad de la población.
La secuenciación adaptativa del repertorio de receptores inmunes (AIRR-seq) identifica los biomarcadores del receptor de células T (TCR) para la especificidad del patógeno y la memoria inmune.
AIRR-seq detecta huellas de infección y ayuda en el estudio de las respuestas individuales al SARS-CoV-2.

Objetivo del estudio:

Desarrollar y aplicar un enfoque de aprendizaje automático para inferir la exposición al SARS-CoV-2 a partir de las características del repertorio de TCR.
Para crear un clasificador robusto para la evaluación de la exposición a COVID-19 utilizando datos AIRR-seq.

Principales métodos:

Aprendizaje automático aplicado a dos grandes conjuntos de datos AIRR-seq (> 1,200 repertorios) de donantes sanos y donantes convalecientes de COVID-19.
Utilizó un nuevo método de corrección de efecto de lote para combinar datos de diferentes lotes y protocolos.
Aseguró datos de alta calidad a través de la estandarización, la tipificación del antígeno leucocitario humano (HLA) y el análisis de las secuencias de cadenas α y β de TCR.

Principales resultados:

Se identificaron características específicas del repertorio de TCR inducidas por la exposición al SARS-CoV-2.
Desarrolló un clasificador robusto y altamente preciso para predecir la exposición a COVID-19.
Demostró la utilidad del aprendizaje automático en el análisis de conjuntos de datos AIRR-seq a gran escala.

Conclusiones:

El clasificador desarrollado es aplicable a repertorios individuales de TCR generados a través de varios protocolos.
Este enfoque permite la evaluación del estado inmune basado en AIRR-seq en grandes cohortes de donantes.
Facilita una aplicación más amplia de AIRR-seq para el monitoreo de enfermedades infecciosas y estudios inmunológicos.

Palabras clave:

El COVID-19 es el covid-19.Los biomarcadores inmunológicos son biomarcadores inmunológicos.El repertorio inmune es el repertorio inmune.La predicción de la predicción del fenotipo.Los receptores de las células T son receptores de células T.La especificidad del TCR.

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