Use of machine learning to identify a T cell response to SARS-CoV-2.
Cell Rep Med
; 2(2): 100192, 2021 02 16.
Article
in English
| MEDLINE | ID: covidwho-1033386
ABSTRACT
The identification of SARS-CoV-2-specific T cell receptor (TCR) sequences is critical for understanding T cell responses to SARS-CoV-2. Accordingly, we reanalyze publicly available data from SARS-CoV-2-recovered patients who had low-severity disease (n = 17) and SARS-CoV-2 infection-naive (control) individuals (n = 39). Applying a machine learning approach to TCR beta (TRB) repertoire data, we can classify patient/control samples with a training sensitivity, specificity, and accuracy of 88.2%, 100%, and 96.4% and a testing sensitivity, specificity, and accuracy of 82.4%, 97.4%, and 92.9%, respectively. Interestingly, the same machine learning approach cannot separate SARS-CoV-2 recovered from SARS-CoV-2 infection-naive individual samples on the basis of B cell receptor (immunoglobulin heavy chain; IGH) repertoire data, suggesting that the T cell response to SARS-CoV-2 may be more stereotyped and longer lived. Following validation in larger cohorts, our method may be useful in detecting protective immunity acquired through natural infection or in determining the longevity of vaccine-induced immunity.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
T-Lymphocytes
/
Machine Learning
/
COVID-19
Type of study:
Cohort study
/
Diagnostic study
/
Observational study
/
Prognostic study
Topics:
Vaccines
Limits:
Humans
Language:
English
Journal:
Cell Rep Med
Year:
2021
Document Type:
Article
Affiliation country:
J.xcrm.2021.100192
Similar
MEDLINE
...
LILACS
LIS